# Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Authors: Wang Zhouhuo
Journal: Security and Communication Networks
 (2021)

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## Abstract

In order to solve the problem of large data classification of human resources, a new parallel classification algorithm of large data of human resources based on the Spark platform is proposed in this study. According to the spark platform, it can complete the update and distance calculation of the human resource big data clustering center and design the big data clustering process. Based on this, the K-means clustering method is introduced to mine frequent itemsets of large data and optimize the aggregation degree of similar large data. A fuzzy genetic algorithm is used to identify the balance of big data. This study adopts the selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resource, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource. The experimental results show that the proposed algorithm has a low time cost, good classification effect, and ideal parallel classification rule complexity.


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## Body

## 1. Introduction

The development and popularization of modern information technology, such as internet technology and computer technology, have promoted the emergence of the new concept of big data and brought society into the era of big data. Doing a good job in the research on the concept and characteristics of big data has become an inevitable choice in future development [1]. Under the big data background, the human resource management is facing a brand new opportunity and the challenge of how through the effective adjustment causes the human resource management to be able to adapt the time development request is the question which the correlation managers must take seriously. Online recruitment has become an important part of human resource recruitment. On the one hand, it can screen resumes for human resources, find the best job fit personnel, and save time and cost [2]; on the other hand, it can improve job search efficiency by providing job recommendations for job seekers. Online recruitment platform needs to be filled in the resume based on the information and the needs of the company's human resources to match the position. It is an important standard to test the recommendation algorithm of human resources to solve the high adaptability of massive data, improve the accuracy of matching, and ensure good feedback efficiency [3, 4].For the classification of big data, reference [5] proposes a design method of big data parallel multilabel k-nearest neighbor classifier based on the Apache Spark framework. In order to reduce the cost consumption of existing MapReduce schemes by using other memory operations, firstly, combined with the parallel mechanism of Apache Spark framework, the training set is divided into several partitions, then found the k-nearest neighbor of each partition of the sample to be predicted in the map stage, and further determined the final k-nearest neighbor according to the results of the map stage in the reduce stage; finally, the nearest neighbor tag sets are aggregated in parallel, and the target tag set of the samples to be predicted is output by maximizing the a posteriori probability. Yang and Zhu [6] propose an intelligent classification method of low occupancy big data under cloud computing. The Bayesian algorithm is used to construct the intelligent classification model so that the fault tolerance can be minimized through the naive Bayesian intelligent classifier in the subsequent classification, and the compression function and feature selection are constructed to train the intelligent classification model with the same degree of discrimination as the source data and classify the features of the source data through the trained classification model. Finally, the purpose of big data intelligent classification under cloud computing is completed. Bensaid and Alimi [7] proposed that big data feature selection plays an important role in learning and classification tasks. The goal is to select relevant nonredundant features. Considering a large number of characteristics in real applications, the FS method using batch learning technology cannot solve the problem of big data, especially when the data arrive in order. Therefore, this study proposes an online feature selection system to solve this problem, that is, an online feature selection method based on multiobjective automatic negotiation, so as to improve the classification performance of the ultra-high-dimensional database. In reference [8], a tree crown MapReduce hybrid parallel language fuzzy rule for big data classification in the cloud is proposed. A parallel language fuzzy rule (lfr-cm) framework based on tree crown mapping reduction is introduced. The lfr-cm framework uses the tree crown MapReduce function to classify big data. However, the above existing literature methods have the problems of chaotic data clustering results and high classification time overhead. Therefore, a new human resource big data parallel classification algorithm based on the Spark platform is designed.According to the proposed method, it adopts selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resources, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource.The main contributions of this study can be described as follows:(1)
We study the key technology of parallel classification algorithm design of big data of human resource, which is now very important but always taken for granted(2)
We study a new method, which is called Spark platform, and we use it to deal with the parallel classification algorithm, which can improve the performance


## 2. Big Data Clustering and Mining Based on Spark Platform

### 2.1. Advantages of Spark Platform Clustering

The Spark platform [9] is an important platform for big data computing following the Hadoop platform and represents a significant advantage in dealing with large-scale data. Like the Hadoop platform, the Spark platform uses the MapReduce computing model for efficient data processing, which solves the time problem of parallel computation of large-scale data but fails to effectively reduce the time cost of complex computation, such as the time cost of iterative computation. In complex computing, iteration is a common computing method. The Spark platform can solve this kind of problem and improve the efficiency of data access by putting iterative computation in memory. The Spark platform's ability to iterate in memory, most importantly because of the use of flexible distributed data sets (RDDs), is also the platform's greatest advantage. All the node memory of the distributed system carries the function of data storage, and there is no need for data access between the external memory, which speeds up the data processing process. In clustering operation, data samples are stored in the memory of each node of the distributed system. Compared with the external memory, the iterative computation is completed in the memory of the node, which saves the time of frequent data interaction from different storage devices.


### 2.2. Clustering Process Design

The memory of all Spark nodes bears the storage cost of data sample calculation. In actual clustering, the update of the cluster center and distance calculation is completed in the RDD of the memory of all Spark nodes. The iterative calculation of the clustering process also goes through the two steps. The longest iteration in the clustering process is completed in memory, which improves the data input and output efficiency. Therefore, the clustering algorithm based on the Spark platform has a time advantage. The clustering process based on the Spark platform is shown in Figure1.Figure 1
Spark platform clustering process.


### 2.3. Big Data Frequent Itemset Mining

Big data frequent itemsets are user-defined itemsets whose number is greater than or equal to that in the data set. Known data setD=T1,T2,…Tv, any Tii=1,…,v represents an event based on itemset Γ, including event id and its opposite itemset X. For an event, assuming that there is an itemset Y and Y⊆X, the event T supports the itemset Y. In set D, all event sets that support itemset Y are called the coverage of Y, which is usually represented by coverY,D=T|T⊆DΛX∈T. The absolute support of Y is the size of the set covering the event, which is described as ΛX,D=coverX,D. Its relative support is the probability that Y occurs in D, that is, the ratio of the absolute support of Y to the data set D, which is described as ΛY,D=ΛY,D/D. The minimum support threshold is known. Assuming ΛrY,D≥λr, the itemset Y belongs to the frequent itemset.In the process of mining large data frequent itemsets, there is an a priori property that can be used to compress the search space. A priori property belongs to all nonempty subsets of frequent itemsets [10, 11]. Combined with this definition, if the itemset X is not frequent, it means that the itemset does not meet the requirements of minimum support. At this time, by adding any items X to B, the frequency of the new itemset X∪A is lower than that of X, which means that X∪A is not frequent.When mining frequent itemsets, in addition to the strategy of compressing the space and reducing the existence of candidates through the characteristics of frequent itemsets, traversing the search space also plays a key role in the statistics of candidate support and the organization of data structure.K-means clustering method [12] uses the FG-growth algorithm to mine the frequent itemsets of big data, eliminate redundant itemsets, and obtain the core frequent itemsets. At the same time, the core itemsets command the centroid of the original clustering of the K-means algorithm and the number of clusters. Using the FG-growth algorithm to mine frequent itemsets reduces the complexity of the K-means algorithm and the influence of outliers on the K-means clustering algorithm.The FG-growth method stores the target data in the highly compressed FP-tree data structure, which reduces the number of traversals. The method constructs FP-tree by counting the element items and mining the frequent itemsets by using the incremental method.The FP-tree belongs to a tree whose root node is empty, and its build rules are as follows:Rule 1: enter the itemset. If the itemset already exists, change the count value in the previous pathRule 2: if the itemset does not exist, you need to create a new path and change the count value of the known pathTo make FP-tree access more efficient, a header pointer table is added, which points to the first instance of a known type, which is interconnected in the same type. When accessed, the elements of a known class can be efficiently accessed based on the header pointer table. To better describe the process of building a tree, the process is described in Figure2.Figure 2
FP-tree structure.The conditional pattern base of large data frequent itemsets is mined in FP-tree, and the mining results are used to establish conditional FP-tree. Conditional FP-tree is a part of FP-tree. Mining big data frequent itemsets by this method is a recursive process. The basic process is as follows.The condition bases of the element itemt are z,x,y,s:2 and z,x,y,r:1, and elements s and r meet the requirements of minimum support, but itemsets t,s and t,r do not meet the conditions of minimum support, so they do not belong to frequent itemsets. The itemsets t,z, t,x, and t,y belong to frequent itemsets. Then, the conditional pattern base of t,z is obtained, and the conditional pattern base is recursively processed to obtain all frequent itemsets.The number of frequent itemsets can describe the number of clusters. Any core frequent itemsets can be the original centroid of K-means [13, 14]. In this algorithm, the threshold is adjusted to know the number of clusters. If the clustering granularity is required to be very high, the threshold can be increased or decreased. The main flow of improved K-means clustering mining large data frequent itemsets is as follows:Step 1: the FP-growth algorithm is used to command the generation of original cluster centroid and quantityStep 2: the original cluster centroid and quantity are taken as the input of K-means, and the mining of large data frequent itemsets is completed


## 2.1. Advantages of Spark Platform Clustering

The Spark platform [9] is an important platform for big data computing following the Hadoop platform and represents a significant advantage in dealing with large-scale data. Like the Hadoop platform, the Spark platform uses the MapReduce computing model for efficient data processing, which solves the time problem of parallel computation of large-scale data but fails to effectively reduce the time cost of complex computation, such as the time cost of iterative computation. In complex computing, iteration is a common computing method. The Spark platform can solve this kind of problem and improve the efficiency of data access by putting iterative computation in memory. The Spark platform's ability to iterate in memory, most importantly because of the use of flexible distributed data sets (RDDs), is also the platform's greatest advantage. All the node memory of the distributed system carries the function of data storage, and there is no need for data access between the external memory, which speeds up the data processing process. In clustering operation, data samples are stored in the memory of each node of the distributed system. Compared with the external memory, the iterative computation is completed in the memory of the node, which saves the time of frequent data interaction from different storage devices.


## 2.2. Clustering Process Design

The memory of all Spark nodes bears the storage cost of data sample calculation. In actual clustering, the update of the cluster center and distance calculation is completed in the RDD of the memory of all Spark nodes. The iterative calculation of the clustering process also goes through the two steps. The longest iteration in the clustering process is completed in memory, which improves the data input and output efficiency. Therefore, the clustering algorithm based on the Spark platform has a time advantage. The clustering process based on the Spark platform is shown in Figure1.Figure 1
Spark platform clustering process.


## 2.3. Big Data Frequent Itemset Mining

Big data frequent itemsets are user-defined itemsets whose number is greater than or equal to that in the data set. Known data setD=T1,T2,…Tv, any Tii=1,…,v represents an event based on itemset Γ, including event id and its opposite itemset X. For an event, assuming that there is an itemset Y and Y⊆X, the event T supports the itemset Y. In set D, all event sets that support itemset Y are called the coverage of Y, which is usually represented by coverY,D=T|T⊆DΛX∈T. The absolute support of Y is the size of the set covering the event, which is described as ΛX,D=coverX,D. Its relative support is the probability that Y occurs in D, that is, the ratio of the absolute support of Y to the data set D, which is described as ΛY,D=ΛY,D/D. The minimum support threshold is known. Assuming ΛrY,D≥λr, the itemset Y belongs to the frequent itemset.In the process of mining large data frequent itemsets, there is an a priori property that can be used to compress the search space. A priori property belongs to all nonempty subsets of frequent itemsets [10, 11]. Combined with this definition, if the itemset X is not frequent, it means that the itemset does not meet the requirements of minimum support. At this time, by adding any items X to B, the frequency of the new itemset X∪A is lower than that of X, which means that X∪A is not frequent.When mining frequent itemsets, in addition to the strategy of compressing the space and reducing the existence of candidates through the characteristics of frequent itemsets, traversing the search space also plays a key role in the statistics of candidate support and the organization of data structure.K-means clustering method [12] uses the FG-growth algorithm to mine the frequent itemsets of big data, eliminate redundant itemsets, and obtain the core frequent itemsets. At the same time, the core itemsets command the centroid of the original clustering of the K-means algorithm and the number of clusters. Using the FG-growth algorithm to mine frequent itemsets reduces the complexity of the K-means algorithm and the influence of outliers on the K-means clustering algorithm.The FG-growth method stores the target data in the highly compressed FP-tree data structure, which reduces the number of traversals. The method constructs FP-tree by counting the element items and mining the frequent itemsets by using the incremental method.The FP-tree belongs to a tree whose root node is empty, and its build rules are as follows:Rule 1: enter the itemset. If the itemset already exists, change the count value in the previous pathRule 2: if the itemset does not exist, you need to create a new path and change the count value of the known pathTo make FP-tree access more efficient, a header pointer table is added, which points to the first instance of a known type, which is interconnected in the same type. When accessed, the elements of a known class can be efficiently accessed based on the header pointer table. To better describe the process of building a tree, the process is described in Figure2.Figure 2
FP-tree structure.The conditional pattern base of large data frequent itemsets is mined in FP-tree, and the mining results are used to establish conditional FP-tree. Conditional FP-tree is a part of FP-tree. Mining big data frequent itemsets by this method is a recursive process. The basic process is as follows.The condition bases of the element itemt are z,x,y,s:2 and z,x,y,r:1, and elements s and r meet the requirements of minimum support, but itemsets t,s and t,r do not meet the conditions of minimum support, so they do not belong to frequent itemsets. The itemsets t,z, t,x, and t,y belong to frequent itemsets. Then, the conditional pattern base of t,z is obtained, and the conditional pattern base is recursively processed to obtain all frequent itemsets.The number of frequent itemsets can describe the number of clusters. Any core frequent itemsets can be the original centroid of K-means [13, 14]. In this algorithm, the threshold is adjusted to know the number of clusters. If the clustering granularity is required to be very high, the threshold can be increased or decreased. The main flow of improved K-means clustering mining large data frequent itemsets is as follows:Step 1: the FP-growth algorithm is used to command the generation of original cluster centroid and quantityStep 2: the original cluster centroid and quantity are taken as the input of K-means, and the mining of large data frequent itemsets is completed


## 3. HR Big Data Parallel Classification Algorithm

### 3.1. Parallel Recognition of Unbalanced Human Resource Data Based on Fuzzy Genetic Algorithm

In the process of effective parallel feature recognition under the big data classification of human resources, a feature model of unbalanced human resource data is constructed. The training samples of unbalanced human resource data are smoothly updated under the state of genetic iteration, and the central point of the transmitted unbalanced data cluster is updated according to the principle of the small square function value. The obtained power spectrum density function of the transmitted unbalanced data is taken as the feature to optimize the features of the transmitted unbalanced data [15]. The process is as follows.Suppose thatX′=x1′,…,xn′ represents the lower set of human resource big data classification, n′ represents the number of data sets X′ transmitted, each element in X′ is an appropriate amount of p′ dimension, X′ has categories, vϕ=vϕ1,…,vϕp′ represents the center of the ϕ category, the input vector under the human resource big data classification obtained at time t is xι1,…,xιm, and the corresponding data type of the vector can be expressed as yϕ. Generally, it is defined as normal human resource big data classification when yϕ=1 and abnormal unbalanced data when yϕ=−1. The effective parallel recognition model of features under human resource big data classification is given by using the following formula:(1)yϕ=fx1,…,xm′.The training samples of transmission unbalanced data are extracted through the feature effective parallel recognition model given in the above formula, and the frequency domain model of unbalanced data is constructed as follows:(2)zt=j⋅at+vϕ⋅yϕϕt,where at represents the corresponding instantaneous amplitude of unbalanced data complex signal zt and ϕt represents the frequency domain resonance amplitude of unbalanced data. The ϕ unbalanced data sample belongs to class j by default, and the corresponding maximum membership is marked as Fϕj.The mean a of the membership degree of all samples of each type of transmission unbalanced data belonging to this category is solved and obtained.Ej=∑Fϕj/Kj and Kj represent the total number of transmission unbalanced data samples of category j. The point set in the high-density area of forged transmission samples is selected as the unbalanced data cluster center set S′, the maximum value sϕ′ in S′ is selected, and this value is regarded as the first cluster center of transmission unbalanced data. Assuming that the initial frequency mean of the transmission unbalanced data is z1 and the corresponding standard deviation is σ^, the unbalanced data training samples are updated and smoothed in the genetic iteration state, and the state space update iteration is carried out by using the following formula:(3)μ^κ+1=sϕ′z1+β^κ1−σ^μ^k.The center point of the training sample cluster of transmission unbalanced data is updated by taking the minimum valueμ^κ+1 of the square difference function as the criterion, and the power spectral density function β^κ is solved to obtain the unbalanced data. This function is regarded as the characteristic of unbalanced data. Suppose L means that there are n″ samples in the transmission unbalanced data set to form the unbalanced data training set, when transmitting unbalanced data, the corresponding sample category ωm′ of c categories is known. The following solution variables are expressed as Q′ and Xϕ to describe the solution of variable Q′ in space. For the corresponding functions of Xϕ, fm′ can be expressed as Xϕ population fitness function. The essence of the optimal solution obtained is to make Xm′ and function value fm′ the maximum value. The uniform random method is used to generate two intersection points swu for transmitting unbalanced human resource data, and the area covered by the intersection of these two points is set as the matching area.(4)εtϕ,j=αtjbjot+1Fϕj∑ϕ=1N∑j=1Nαtjbjot+1Fϕj,where αtj represents the communication path deviation of the monitoring node transmitting unbalanced human resource data, and bjot+1 represents the Gaussian process with a mean value of 0 and variance of 1. The information fusion filter function of transmission unbalanced data node is designed:(5)Hf=Sdf/εtϕ,j.Among them,Sdf is used to describe the Doppler power spectrum. A genetic algorithm is used to iterate the characteristics of transmission unbalanced data to realize the feature optimization of transmission unbalanced data. The genetic iterative search dispersion formula of transmission unbalanced data is given by using the following formula:(6)prϕt=pt⋅hϕt+f,where hϕt represents the variation parameter of hϕt in the process of transmission unbalanced human resource data feature mining and f represents the characteristic response function of transmission unbalanced human resource data.


### 3.2. Effective Parallel Recognition Method of Human Resource Big Data Features

In the process of effective parallel feature recognition under the big data classification of human resources, based on the result of feature optimization of unbalanced human resource data, the selective integration method is adopted to study the unbalanced database classifier in the transmission process, and the model of abnormal support degree of the transmitted unbalanced database classifier is constructed by introducing the decision profile matrix [16, 17]. The support entropy is used to measure the category support degree of the classification decision matrix of the transmitted unbalanced data and to solve the fuzzy difference degree problem among each classifier set. The specific process is described as follows.Suppose thatF represents a feature vector of unbalanced data, Ω=ε1,…,εν represents a set of labels of unbalanced data, and the output support of classifiers Dν in the integrated learning method is D=D1,…,DL. dν,οx means that the classifier Dν assumes that the nonequilibrium data F originate from the support of category υν, and the greater the support, the more likely it is to become a category label υν. Input F for a given unbalanced HR data sets the output of classifier [18] to the support matrix DF of a base classifier, where(7)DF=d1,1fd1,1fd1,υf⋮dν,1fdν,οfdν,υf.The output value of the classifierDνf is described by the decision-making profile matrix of the unbalanced human resource data in the transmission process, and the values dν,οf and dν,υf represent the elements of the matrix, and the columns represent the corresponding support degree of the classifier Dυ output class υo. The sparse matrix is adopted to describe the relationship combination between the base classifier and the integration classifier of the unbalanced data transmission, where(8)RF=r1,1xr1,1xr1,υx⋮rψ,1xrψ,oxrψ,υx.Among them,rν,ox and ν=1,…,ψ,o=1,…,υ are used to describe whether the base classifier o exists in the ν classifier and rψ,υx is used to describe whether the base classifier υ exists in the ψ classifier.The method of variance measure is used to select and integrate the unbalanced data in the process of transmission. The precondition of selecting and integrating is to evaluate the support of each classifier set to the unbalanced human resource data set and calculate by using the following formula:(9)gCi=−∑jndikSiIndijSi.Among them,Ci represents the set of i-base classifiers for unbalanced data, dij describes the support of i-base classifiers for the attributes of the class j of unbalanced data, n¨ represents the number of sets of base classifiers for unbalanced data, m¨ represents the number of categories of attributes to be classified for unbalanced data, and Si represents the total support of each set of classifiers for a single instance of unbalanced data classification.Multiple observations are used to integrate the differences of classifier class tags of the unbalanced database in the transmission process, and exponential weighting is used to identify the current support entropy of unbalanced data unit time in parallel, namely,(10)h¯t=aht−1+1−ah¯t−1,t>2,h1,t=2..In the formula,h¯t−1 represents the parallel identification entropy value of unbalanced data transmitted at t−1 time, a represents the ratio between the dimension size of data and the overall dimension size, and ht−1 represents the actual entropy value of unbalanced data transmitted at t−1 time, from which the parallel identification entropy value h¯t of unbalanced data transmitted at t time can be obtained.The dimension size of human resource big data classification affects the support entropy parallel recognition to a certain extent and adjusts the dimension size according to the change ofa value, κ represents the support entropy standard deviation of t unit time unbalanced human resource data classifier set i, κ is used to describe the threshold of t unit time unbalanced human resource database classifier i for the effective parallel recognition difference degree of classification features, and the following:(11)κ=1ψ−1∑j=t−ψiht−h¯t−12,(12)δt,i=h¯t,i±6σt,i.Among them,h¯t,i−6σt,i,h¯t,i+6σt,i represents the confidence interval range of the classification of unbalanced human resource data during the transmission of the set i¨ of classifier t unit time.According to the above steps, the parallel recognition of the sequence under the big data classification is completed.


### 3.3. Association Repair of HR Big Data

At the beginning of parallel classification of human resource big data, the weak association data are first repaired by using the real-time data in the process of association query, and then, the repairable association features are used to establish the most probable association of human resource data, so as to increase the association of massive human resource data in the database under the weak association rules, and so as to facilitate the parallel classification of human resource data in the next step.Due to the huge amount of human resource data in the database, there are a large number of redundant human resource data, which leads to the shortage of a large amount of human resource data and little information. The existence of these weak association attributes brings great difficulty to information search, which needs to be repeated several times to complete. If the correlation is too weak, even cannot query the required information, it seriously affects the performance of the database. In order to ensure the efficiency of the query of large database information, it is necessary to repair the relationship between a weakened association of human resources data to ensure smooth query work. The repair conditions are as follows.A parameter is set as the judgment value of whether each HR data in the large database need to be repaired, and the value is set tom. When m=1, the HR data need to be disconnected for repair, when m=2, the HR data need to be weakly associated for repair, and when m=3, the HR data need to be expanded and simplified for repair. By calculating the probability distribution of m in ξKm, we can get whether the nonfixed setting is required. K represents the HR data to be queried, and the value of K represents the status of the HR data.(1)
K=0 means that there are no HR data to be queried in the database, and only the lost HR data need to be patched; then, the value of ξKm is set to 0.(2)
WhenK=1, it indicates that HR big data exist in the database. The value is randomly set, and the range ξ1m〉0 needs to be set.(3)
WhenK≠0 and K≠1 and index probability ξKm〉0, all types of repair are required.


### 3.4. Application of Improved Ant Colony Algorithm in Parallel Classification of Human Resource Data

After updating the weak association rules, the TSP problem is combined with an ant colony algorithm to find an optimal algorithm for the parallel classification of human resource data.In the parallel classification of human resource data,IF−THEN rules must be extracted from the training set, and all attribute fields in the database are traversed by an ant colony algorithm to set up the decision rules. The specific process is described as follows:(1)
Any human resource data is selected in the database to form a training set.(2)
The pheromone of the ant colony algorithm is initially set.(3)
The ant is placed in the database and traversed through all the attributes in the database; then, the required attributes are selected to establish a parallel classification rule.(4)
The attributes irrelevant to the inquiry information are filtered out in the rules established in the previous step.(5)
Pheromones based on the maturity of established rules are added or subtracted, and if the query jumps, the process continues, otherwise step 3 will be returned.(6)
The parallel classification rules extracted from the above steps are placed in the rule list, and when the rules in the list overwrite all the attributes of the elements in the training set, the program ends; otherwise, return to step 2. First, the pheromones contained in eachtermij should be initially set as follows (13).(13)τij=1∑i−1αbi.In the formula,α represents the number of human resource data attributes in the database, and bi represents the number of elements in the set of Ai attribute values.The pheromone on eachtermij is updated when the ant has completed the traversal of all the attribute sets in the database. The update of pheromone is the increase or decrease in the pheromone content, and the judgment of the increase or decrease is decided by the rules constructed by ants after traversing the set of attributes. The incremental size of the pheromone is determined by the attribute coverage Q of the training set by a parallel classification rule established by the ant after traversing all attributes, as shown in type (14):(14)Q=TPTP−FN×TNFP+TN,where TP represents the number of HR data covered by the parallel classification rules and judged as correct by the rules and FP represents the number of HR data covered but judged as incorrect by the rules. TN represents the number of HR data not covered by the rules and determined to be incorrect by the rules, and FN represents the number of HR data not covered by the rules but determined to be correct by the rules.When using the ant colony algorithm to complete the parallel classification of human resource data, it is necessary to introduce a contribution function to avoid premature convergence of the algorithm and unreasonable allocation of pheromones.


### 3.5. Improvement of Parallel Classification Rules

The improvement of parallel classification rules is mainly accomplished by postprocessing the rules, which can simplify the rule list by deleting the irrelevant attributes in the rule list, make it express the attributes of human resource data in the database better, and then make the rule list to more accurately judge the attributes of querying human resource data. Moreover, by removing the rules that are not relevant to the required attributes, the algorithm can be made easier to understand and use.Usually, after the rule list is established by the ant colony algorithm, the rule list must be simplified. For a rule in a rule list, its deletion criterion is to judge whether the rule is conducive to expressing the attribute of the information queried. If the attribute does not contribute to the attribute expression, the influence of the attribute on the quality of the rule list is negative. In the part of rule list improvement, we select the coverage parameterQ of the human resource data set mentioned in the previous section to judge the impact of rules on the list. The process is a cyclic process until each rule is judged to end after the cyclic process to simplify the parallel classification of human resource big data.


## 3.1. Parallel Recognition of Unbalanced Human Resource Data Based on Fuzzy Genetic Algorithm

In the process of effective parallel feature recognition under the big data classification of human resources, a feature model of unbalanced human resource data is constructed. The training samples of unbalanced human resource data are smoothly updated under the state of genetic iteration, and the central point of the transmitted unbalanced data cluster is updated according to the principle of the small square function value. The obtained power spectrum density function of the transmitted unbalanced data is taken as the feature to optimize the features of the transmitted unbalanced data [15]. The process is as follows.Suppose thatX′=x1′,…,xn′ represents the lower set of human resource big data classification, n′ represents the number of data sets X′ transmitted, each element in X′ is an appropriate amount of p′ dimension, X′ has categories, vϕ=vϕ1,…,vϕp′ represents the center of the ϕ category, the input vector under the human resource big data classification obtained at time t is xι1,…,xιm, and the corresponding data type of the vector can be expressed as yϕ. Generally, it is defined as normal human resource big data classification when yϕ=1 and abnormal unbalanced data when yϕ=−1. The effective parallel recognition model of features under human resource big data classification is given by using the following formula:(1)yϕ=fx1,…,xm′.The training samples of transmission unbalanced data are extracted through the feature effective parallel recognition model given in the above formula, and the frequency domain model of unbalanced data is constructed as follows:(2)zt=j⋅at+vϕ⋅yϕϕt,where at represents the corresponding instantaneous amplitude of unbalanced data complex signal zt and ϕt represents the frequency domain resonance amplitude of unbalanced data. The ϕ unbalanced data sample belongs to class j by default, and the corresponding maximum membership is marked as Fϕj.The mean a of the membership degree of all samples of each type of transmission unbalanced data belonging to this category is solved and obtained.Ej=∑Fϕj/Kj and Kj represent the total number of transmission unbalanced data samples of category j. The point set in the high-density area of forged transmission samples is selected as the unbalanced data cluster center set S′, the maximum value sϕ′ in S′ is selected, and this value is regarded as the first cluster center of transmission unbalanced data. Assuming that the initial frequency mean of the transmission unbalanced data is z1 and the corresponding standard deviation is σ^, the unbalanced data training samples are updated and smoothed in the genetic iteration state, and the state space update iteration is carried out by using the following formula:(3)μ^κ+1=sϕ′z1+β^κ1−σ^μ^k.The center point of the training sample cluster of transmission unbalanced data is updated by taking the minimum valueμ^κ+1 of the square difference function as the criterion, and the power spectral density function β^κ is solved to obtain the unbalanced data. This function is regarded as the characteristic of unbalanced data. Suppose L means that there are n″ samples in the transmission unbalanced data set to form the unbalanced data training set, when transmitting unbalanced data, the corresponding sample category ωm′ of c categories is known. The following solution variables are expressed as Q′ and Xϕ to describe the solution of variable Q′ in space. For the corresponding functions of Xϕ, fm′ can be expressed as Xϕ population fitness function. The essence of the optimal solution obtained is to make Xm′ and function value fm′ the maximum value. The uniform random method is used to generate two intersection points swu for transmitting unbalanced human resource data, and the area covered by the intersection of these two points is set as the matching area.(4)εtϕ,j=αtjbjot+1Fϕj∑ϕ=1N∑j=1Nαtjbjot+1Fϕj,where αtj represents the communication path deviation of the monitoring node transmitting unbalanced human resource data, and bjot+1 represents the Gaussian process with a mean value of 0 and variance of 1. The information fusion filter function of transmission unbalanced data node is designed:(5)Hf=Sdf/εtϕ,j.Among them,Sdf is used to describe the Doppler power spectrum. A genetic algorithm is used to iterate the characteristics of transmission unbalanced data to realize the feature optimization of transmission unbalanced data. The genetic iterative search dispersion formula of transmission unbalanced data is given by using the following formula:(6)prϕt=pt⋅hϕt+f,where hϕt represents the variation parameter of hϕt in the process of transmission unbalanced human resource data feature mining and f represents the characteristic response function of transmission unbalanced human resource data.


## 3.2. Effective Parallel Recognition Method of Human Resource Big Data Features

In the process of effective parallel feature recognition under the big data classification of human resources, based on the result of feature optimization of unbalanced human resource data, the selective integration method is adopted to study the unbalanced database classifier in the transmission process, and the model of abnormal support degree of the transmitted unbalanced database classifier is constructed by introducing the decision profile matrix [16, 17]. The support entropy is used to measure the category support degree of the classification decision matrix of the transmitted unbalanced data and to solve the fuzzy difference degree problem among each classifier set. The specific process is described as follows.Suppose thatF represents a feature vector of unbalanced data, Ω=ε1,…,εν represents a set of labels of unbalanced data, and the output support of classifiers Dν in the integrated learning method is D=D1,…,DL. dν,οx means that the classifier Dν assumes that the nonequilibrium data F originate from the support of category υν, and the greater the support, the more likely it is to become a category label υν. Input F for a given unbalanced HR data sets the output of classifier [18] to the support matrix DF of a base classifier, where(7)DF=d1,1fd1,1fd1,υf⋮dν,1fdν,οfdν,υf.The output value of the classifierDνf is described by the decision-making profile matrix of the unbalanced human resource data in the transmission process, and the values dν,οf and dν,υf represent the elements of the matrix, and the columns represent the corresponding support degree of the classifier Dυ output class υo. The sparse matrix is adopted to describe the relationship combination between the base classifier and the integration classifier of the unbalanced data transmission, where(8)RF=r1,1xr1,1xr1,υx⋮rψ,1xrψ,oxrψ,υx.Among them,rν,ox and ν=1,…,ψ,o=1,…,υ are used to describe whether the base classifier o exists in the ν classifier and rψ,υx is used to describe whether the base classifier υ exists in the ψ classifier.The method of variance measure is used to select and integrate the unbalanced data in the process of transmission. The precondition of selecting and integrating is to evaluate the support of each classifier set to the unbalanced human resource data set and calculate by using the following formula:(9)gCi=−∑jndikSiIndijSi.Among them,Ci represents the set of i-base classifiers for unbalanced data, dij describes the support of i-base classifiers for the attributes of the class j of unbalanced data, n¨ represents the number of sets of base classifiers for unbalanced data, m¨ represents the number of categories of attributes to be classified for unbalanced data, and Si represents the total support of each set of classifiers for a single instance of unbalanced data classification.Multiple observations are used to integrate the differences of classifier class tags of the unbalanced database in the transmission process, and exponential weighting is used to identify the current support entropy of unbalanced data unit time in parallel, namely,(10)h¯t=aht−1+1−ah¯t−1,t>2,h1,t=2..In the formula,h¯t−1 represents the parallel identification entropy value of unbalanced data transmitted at t−1 time, a represents the ratio between the dimension size of data and the overall dimension size, and ht−1 represents the actual entropy value of unbalanced data transmitted at t−1 time, from which the parallel identification entropy value h¯t of unbalanced data transmitted at t time can be obtained.The dimension size of human resource big data classification affects the support entropy parallel recognition to a certain extent and adjusts the dimension size according to the change ofa value, κ represents the support entropy standard deviation of t unit time unbalanced human resource data classifier set i, κ is used to describe the threshold of t unit time unbalanced human resource database classifier i for the effective parallel recognition difference degree of classification features, and the following:(11)κ=1ψ−1∑j=t−ψiht−h¯t−12,(12)δt,i=h¯t,i±6σt,i.Among them,h¯t,i−6σt,i,h¯t,i+6σt,i represents the confidence interval range of the classification of unbalanced human resource data during the transmission of the set i¨ of classifier t unit time.According to the above steps, the parallel recognition of the sequence under the big data classification is completed.


## 3.3. Association Repair of HR Big Data

At the beginning of parallel classification of human resource big data, the weak association data are first repaired by using the real-time data in the process of association query, and then, the repairable association features are used to establish the most probable association of human resource data, so as to increase the association of massive human resource data in the database under the weak association rules, and so as to facilitate the parallel classification of human resource data in the next step.Due to the huge amount of human resource data in the database, there are a large number of redundant human resource data, which leads to the shortage of a large amount of human resource data and little information. The existence of these weak association attributes brings great difficulty to information search, which needs to be repeated several times to complete. If the correlation is too weak, even cannot query the required information, it seriously affects the performance of the database. In order to ensure the efficiency of the query of large database information, it is necessary to repair the relationship between a weakened association of human resources data to ensure smooth query work. The repair conditions are as follows.A parameter is set as the judgment value of whether each HR data in the large database need to be repaired, and the value is set tom. When m=1, the HR data need to be disconnected for repair, when m=2, the HR data need to be weakly associated for repair, and when m=3, the HR data need to be expanded and simplified for repair. By calculating the probability distribution of m in ξKm, we can get whether the nonfixed setting is required. K represents the HR data to be queried, and the value of K represents the status of the HR data.(1)
K=0 means that there are no HR data to be queried in the database, and only the lost HR data need to be patched; then, the value of ξKm is set to 0.(2)
WhenK=1, it indicates that HR big data exist in the database. The value is randomly set, and the range ξ1m〉0 needs to be set.(3)
WhenK≠0 and K≠1 and index probability ξKm〉0, all types of repair are required.


## 3.4. Application of Improved Ant Colony Algorithm in Parallel Classification of Human Resource Data

After updating the weak association rules, the TSP problem is combined with an ant colony algorithm to find an optimal algorithm for the parallel classification of human resource data.In the parallel classification of human resource data,IF−THEN rules must be extracted from the training set, and all attribute fields in the database are traversed by an ant colony algorithm to set up the decision rules. The specific process is described as follows:(1)
Any human resource data is selected in the database to form a training set.(2)
The pheromone of the ant colony algorithm is initially set.(3)
The ant is placed in the database and traversed through all the attributes in the database; then, the required attributes are selected to establish a parallel classification rule.(4)
The attributes irrelevant to the inquiry information are filtered out in the rules established in the previous step.(5)
Pheromones based on the maturity of established rules are added or subtracted, and if the query jumps, the process continues, otherwise step 3 will be returned.(6)
The parallel classification rules extracted from the above steps are placed in the rule list, and when the rules in the list overwrite all the attributes of the elements in the training set, the program ends; otherwise, return to step 2. First, the pheromones contained in eachtermij should be initially set as follows (13).(13)τij=1∑i−1αbi.In the formula,α represents the number of human resource data attributes in the database, and bi represents the number of elements in the set of Ai attribute values.The pheromone on eachtermij is updated when the ant has completed the traversal of all the attribute sets in the database. The update of pheromone is the increase or decrease in the pheromone content, and the judgment of the increase or decrease is decided by the rules constructed by ants after traversing the set of attributes. The incremental size of the pheromone is determined by the attribute coverage Q of the training set by a parallel classification rule established by the ant after traversing all attributes, as shown in type (14):(14)Q=TPTP−FN×TNFP+TN,where TP represents the number of HR data covered by the parallel classification rules and judged as correct by the rules and FP represents the number of HR data covered but judged as incorrect by the rules. TN represents the number of HR data not covered by the rules and determined to be incorrect by the rules, and FN represents the number of HR data not covered by the rules but determined to be correct by the rules.When using the ant colony algorithm to complete the parallel classification of human resource data, it is necessary to introduce a contribution function to avoid premature convergence of the algorithm and unreasonable allocation of pheromones.


## 3.5. Improvement of Parallel Classification Rules

The improvement of parallel classification rules is mainly accomplished by postprocessing the rules, which can simplify the rule list by deleting the irrelevant attributes in the rule list, make it express the attributes of human resource data in the database better, and then make the rule list to more accurately judge the attributes of querying human resource data. Moreover, by removing the rules that are not relevant to the required attributes, the algorithm can be made easier to understand and use.Usually, after the rule list is established by the ant colony algorithm, the rule list must be simplified. For a rule in a rule list, its deletion criterion is to judge whether the rule is conducive to expressing the attribute of the information queried. If the attribute does not contribute to the attribute expression, the influence of the attribute on the quality of the rule list is negative. In the part of rule list improvement, we select the coverage parameterQ of the human resource data set mentioned in the previous section to judge the impact of rules on the list. The process is a cyclic process until each rule is judged to end after the cyclic process to simplify the parallel classification of human resource big data.


## 4. Experimental Results and Analysis

### 4.1. Experimental Results

In order to prove the effectiveness of the designed human resource big data parallel classification method, a simulation experiment is needed. Taking the big data intelligent classification algorithm based on cloud computing proposed in reference [6], the big data classification algorithm based on big data feature selection proposed in reference [7], and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] as the control group, the performances of different algorithms are compared through the analysis of experimental results. First, the operating parameters of the experimental platform are set, as shown in Table 1.Table 1
Operating parameters of experimental platform.
Parameters and platformParameter valueProcessorP4 2.8cMemory584MOperating systemWindows XP + sp2Programing languageJava 1.5Number of ants9000Number of candidate sets100Maximum number of cycles50Six human resource data sets from the UCI Machine Learning Repository are selected to be simulated with improved methods and traditional methods to demonstrate the accuracy and complexity of the proposed methods and to improve the effectiveness of the proposed methods. The experimental human resource data set is shown in Table2.Table 2
Experimental human resource data set.
Data settingsData quantityDiscrete attributeContinuous attributeNumber of classesHuman resource category 1330813Human resource category 2722373Human resource category 31076413Human resource category 46242213Human resource category 53171063Human resource category 62858763Based on the obtained human resource data set, the experimental analysis is carried out. The improved algorithm and the traditional algorithm are, respectively, used for parallel classification of the human resource data set used in the experiment, and the parallel classification results are shown in Table3.Table 3
Comparison of accuracy of parallel classification of big data.
Types of human resourcesComplexity of the proposed algorithm (%)Complexity of big data intelligent classification algorithm based on cloud computing (%)Complexity of big data classification algorithm based on big data feature selection (%)Complexity of big data classification algorithm based on parallel language fuzzy rules (%)MeanDeviationMeanDeviationMeanDeviationMeanDeviation157.232.1371.454.5870.222.5768.275.59290.740.5791.563.5490.554.2691.264.52391.680.2270.294.5569.374.7072.964.23488.231.4191.756.9692.754.2692.445.74582.251.2789.203.9788.663.7785.222.76642.171.0655.212.8554.203.7554.695.20It can be seen from Table3 that the maximum error of the improved method is 2.13%, and the errors of the literature methods are higher than those of the improved method. This is because the improved algorithm establishes the parallel classification standard of massive human resource big data by analyzing the necessary conditions for massive human resource big data training and the distribution function of aperiodic tasks. The comparison of the complexity of algorithm parallel classification rules is shown in Table 4.Table 4
Comparison of the complexity of algorithm parallel classification rules.
Types of human resourcesComplexity of the proposed algorithm (%)Complexity of big data intelligent classification algorithm based on cloud computing (%)Complexity of big data classification algorithm based on big data feature selection (%)Complexity of big data classification algorithm based on parallel language fuzzy rules (%)18.515.4156.742.4125.325.2315452.0532.645.7938.6242.1748.536.5419.644.8957.644.2018.750.2069.230.4736.830.47Simulation results show that the proposed algorithm is not only more accurate than the traditional parallel classification algorithm but also more simple and efficient than the traditional method.The classification overhead is the main index to measure the classification performance, and the classification time overhead of different algorithms is calculated. The result is shown in Figure3.Figure 3
Comparison of classification overhead for different algorithms.According to the test results in Figure3, the cost of the proposed algorithm is lower than 30 ms, but the cost of the proposed algorithm is relatively stable and does not obviously increase. It is shown that the proposed method can effectively reduce the classification delay and time overhead and has high applicability.


### 4.2. Human Resource Big Data Classification Effect Test

It is known that the selected human resource big data are divided into three categories: high-quality resources, ordinary resources, and poor-quality resources. The big data intelligent classification algorithm based on cloud computing proposed in reference [6], the big data classification algorithm based on big data feature selection proposed in reference [7], and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] are used as the control group. As the control group, the algorithm designed in this study is used as the experimental group. Figure 4 shows the effect of human resource big data classification of four groups of algorithms.Figure 4
HR data classification effect. (a) Designed algorithm. (b) Big data intelligent classification algorithm based on cloud computing. (c) Big data classification algorithm based on big data feature selection. (d) Big data classification algorithm based on parallel language fuzzy rules.
(a)(b)(c)(d)According to Figure4, there are many differences in the classification effects of different algorithms on human resource big data. The classification results of the big data intelligent classification algorithm based on cloud computing proposed in reference [6] have some isolated data, resulting in the problem of data omission in big data classification. The classification results of the big data classification algorithm based on big data feature selection proposed in reference [7] and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] are chaotic, and the classification accuracy is not ideal. In contrast, the designed algorithm can classify three types of human resource big data with high accuracy and good application performance.


## 4.1. Experimental Results

In order to prove the effectiveness of the designed human resource big data parallel classification method, a simulation experiment is needed. Taking the big data intelligent classification algorithm based on cloud computing proposed in reference [6], the big data classification algorithm based on big data feature selection proposed in reference [7], and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] as the control group, the performances of different algorithms are compared through the analysis of experimental results. First, the operating parameters of the experimental platform are set, as shown in Table 1.Table 1
Operating parameters of experimental platform.
Parameters and platformParameter valueProcessorP4 2.8cMemory584MOperating systemWindows XP + sp2Programing languageJava 1.5Number of ants9000Number of candidate sets100Maximum number of cycles50Six human resource data sets from the UCI Machine Learning Repository are selected to be simulated with improved methods and traditional methods to demonstrate the accuracy and complexity of the proposed methods and to improve the effectiveness of the proposed methods. The experimental human resource data set is shown in Table2.Table 2
Experimental human resource data set.
Data settingsData quantityDiscrete attributeContinuous attributeNumber of classesHuman resource category 1330813Human resource category 2722373Human resource category 31076413Human resource category 46242213Human resource category 53171063Human resource category 62858763Based on the obtained human resource data set, the experimental analysis is carried out. The improved algorithm and the traditional algorithm are, respectively, used for parallel classification of the human resource data set used in the experiment, and the parallel classification results are shown in Table3.Table 3
Comparison of accuracy of parallel classification of big data.
Types of human resourcesComplexity of the proposed algorithm (%)Complexity of big data intelligent classification algorithm based on cloud computing (%)Complexity of big data classification algorithm based on big data feature selection (%)Complexity of big data classification algorithm based on parallel language fuzzy rules (%)MeanDeviationMeanDeviationMeanDeviationMeanDeviation157.232.1371.454.5870.222.5768.275.59290.740.5791.563.5490.554.2691.264.52391.680.2270.294.5569.374.7072.964.23488.231.4191.756.9692.754.2692.445.74582.251.2789.203.9788.663.7785.222.76642.171.0655.212.8554.203.7554.695.20It can be seen from Table3 that the maximum error of the improved method is 2.13%, and the errors of the literature methods are higher than those of the improved method. This is because the improved algorithm establishes the parallel classification standard of massive human resource big data by analyzing the necessary conditions for massive human resource big data training and the distribution function of aperiodic tasks. The comparison of the complexity of algorithm parallel classification rules is shown in Table 4.Table 4
Comparison of the complexity of algorithm parallel classification rules.
Types of human resourcesComplexity of the proposed algorithm (%)Complexity of big data intelligent classification algorithm based on cloud computing (%)Complexity of big data classification algorithm based on big data feature selection (%)Complexity of big data classification algorithm based on parallel language fuzzy rules (%)18.515.4156.742.4125.325.2315452.0532.645.7938.6242.1748.536.5419.644.8957.644.2018.750.2069.230.4736.830.47Simulation results show that the proposed algorithm is not only more accurate than the traditional parallel classification algorithm but also more simple and efficient than the traditional method.The classification overhead is the main index to measure the classification performance, and the classification time overhead of different algorithms is calculated. The result is shown in Figure3.Figure 3
Comparison of classification overhead for different algorithms.According to the test results in Figure3, the cost of the proposed algorithm is lower than 30 ms, but the cost of the proposed algorithm is relatively stable and does not obviously increase. It is shown that the proposed method can effectively reduce the classification delay and time overhead and has high applicability.


## 4.2. Human Resource Big Data Classification Effect Test

It is known that the selected human resource big data are divided into three categories: high-quality resources, ordinary resources, and poor-quality resources. The big data intelligent classification algorithm based on cloud computing proposed in reference [6], the big data classification algorithm based on big data feature selection proposed in reference [7], and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] are used as the control group. As the control group, the algorithm designed in this study is used as the experimental group. Figure 4 shows the effect of human resource big data classification of four groups of algorithms.Figure 4
HR data classification effect. (a) Designed algorithm. (b) Big data intelligent classification algorithm based on cloud computing. (c) Big data classification algorithm based on big data feature selection. (d) Big data classification algorithm based on parallel language fuzzy rules.
(a)(b)(c)(d)According to Figure4, there are many differences in the classification effects of different algorithms on human resource big data. The classification results of the big data intelligent classification algorithm based on cloud computing proposed in reference [6] have some isolated data, resulting in the problem of data omission in big data classification. The classification results of the big data classification algorithm based on big data feature selection proposed in reference [7] and the big data classification algorithm based on parallel language fuzzy rules proposed in reference [8] are chaotic, and the classification accuracy is not ideal. In contrast, the designed algorithm can classify three types of human resource big data with high accuracy and good application performance.


## 5. Conclusions

At present, the widely used human resource big data classification algorithms have the problems of unsatisfactory data clustering effect and long classification time. In order to improve this disadvantage, a parallel human resource big data classification algorithm based on the Spark platform is designed. Through the calculation of cluster center update and distance on the Spark platform, the clustering effect of big data is optimized. The unbalanced big data are filtered out, the unbalanced human resource data classifier is learned in the transmission process through selective integration, the parallel recognition of human resource big data features is completed, and the efficiency of parallel classification of big data is improved. The correlation between human resource big data is repaired, and the efficient classification of human resource big data is realized. The experimental results show that the designed algorithm has a better classification effect, can complete the big data task in a shorter time, and has reliable practicability.



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*Source: 5811918-2021-12-26.xml*