# Research on Enterprise Data Management Strategy Analysis System Based on Knowledge Mining Model

Authors: Yongkang Li; Boyan Duan
Journal: Mobile Information Systems
 (2022)

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## Abstract

The aim of this study is to solve the problem of low decision-making efficiency, this paper discusses the method of decision analysis based on business application data and it puts forward the enterprise data management system based on knowledge mining. The system uses the improved decision tree algorithm for data management, which optimizes the selection of the best threshold segmentation point in the process of discretizing the continuous value feature attributes. An improved decision tree algorithm is proposed and applied to enterprise data management. A method to optimize threshold segmentation points is proposed, which reduces the partition of threshold points and reduces the time complexity of the algorithm. The experimental results show that the operation efficiency and classification accuracy of the model on the abalone dataset are due to the traditional C4.5 algorithm, which can effectively deal with the analysis and management knowledge mining of large-scale enterprise operation data.


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## Body

## 1. Introduction

Human society has entered the era of the knowledge economy, that economic trend of informatization, networking, technology, and globalization has brought about fundamental changes in the business environment of enterprises. Knowledge and intellectual capital are increasingly replacing the dominant position of material assets as the main factor to measure the competitiveness of enterprises. With the advent of the knowledge economy, the role and significance of knowledge management are becoming increasingly important in the process of enterprises from becoming capital-intensive to knowledge-intensive. While improving the theoretical system and structure of knowledge management, it promotes the development of knowledge management applications through the combination of theories and methods of technical information, management science, data statistics, and other disciplines [1, 2].Many enterprises have accumulated certain information resources and established their own information sharing and management systems. However, the use of this information only stays at the level of sharing and browsing, as well as the simple retrieval function of the Department’s data, so it is difficult to make in-depth analysis and utilization [3, 4]. The OA system focuses on the flow of workflow in the design of information organization structure rather than the vein structure of knowledge. It is difficult to use the structure of OA directly for knowledge mining and service, which is why it is necessary to restructure the structure.With the rapid development of the Internet, Internet of things, and artificial intelligence devices, data collection of enterprises becomes more and more simple, while the collected data sets become more and more complex and huge. No matter the characteristic dimension of data or the number of samples, they are growing exponentially, often forming large-scale or even super large-scale data volumes. For example, radio frequency identification, bar codes, and other real-time monitoring technologies are widely used in the production process, and the granularity of the collected data related to the manufacturing process is becoming more and more detailed. Resulting in the rapid increase of data dimension, as well as the proportion of unstructured data composed of text, voice, picture and video is increasing, which brings about the increasing number of data with multilabel features [5]. After entering the stage of computer-aided management, literature [6] held the view that the related technologies and methods of data mining will play an important role in processing massive, high-dimensional, and complex-quality data. The applications in product quality management include: quality prediction and quality classification, the former is the most widely used direction. Based on the integration strategy, reference [7] proposed an optical channel transmission quality prediction method, which improved the problem of insufficient accuracy of the optical channel transmission quality prediction method in dynamic wide area optical backbone networks, so it had excellent nonlinear approximation performance. In reference [8], fuzzy optimization method was used to highlight the process uncertainty and time uncertainty of complex product development, and the task execution time was represented by a fuzzy number. The mapping relationship between the knowledge structure of a work unit and the similarity of task knowledge requirements was established, so the GA algorithm was used to solve the problem; literature [9] adopts multiobjective optimization method; aiming at the manufacturing task allocation problem in intelligent manufacturing environment, considering the service competency and collaboration level, a multiobjective optimization model is constructed, so that the improved POS algorithm and weighted TOPSIS decision algorithm are used to solve the problem; in order to find the optimal number of tasks in each class, we use the two-sided clustering method to find the optimal number of tasks in each class.Currently, the basic task of the knowledge mining is data classification, data clustering, recession and prediction, data association, data description and recognition, knowledge mining technology, mainly, shows the dig, accurate identification, scientific extraction, dynamic management, and the characteristics of the intelligent classification, all information in the database to make use of the technology can be detailed, comprehensive, and systematic analysis. It can assist knowledge management work such as business financial management, customer relationship management, supply chain management, and enterprise development planning. Due to the different needs and users’ implicit behavior, users’ demand for knowledge services is less affected by many factors such as geographical location, so that the existing knowledge push technology cannot form effective support for enterprise knowledge services, but also block knowledge sharing and pooling between enterprises. The high cost and slow speed of updating large volumes of data will lead to backward management knowledge and wrong decision-making, which will greatly reduce the competitiveness of enterprises; Moreover, there is the problem of category imbalance in enterprise operation data, that is, the proportion of data related to the problems concerned by managers is very small, which makes it difficult to analyze the causes and development trend.Therefore, this paper introduces the method and model of data mining into knowledge transfer, and combines the qualitative analysis of knowledge transfer with the quantitative simulation of knowledge mining. In this paper, the methods of information data classification processing and the method of decision analysis using the data generated by business applications are discussed, and an enterprise data management system based on knowledge mining is proposed. The method optimizes the selection of the best threshold segmentation point in the process of discretizing the continuous value feature attributes, which makes a breakthrough and improvement in the concept of knowledge management. An improved decision tree algorithm is proposed and applied to enterprise data management. A method to optimize threshold segmentation points is proposed, which reduces the partition of threshold points and reduces the time complexity of the algorithm. Based on the platform, the task is planned and decomposed, and the discrete task activity nodes are assigned to multiple enterprises to complete cooperatively, so as to improve the enterprise’s decision-making ability of business information.


## 2. Design of Enterprise Data Management System Based on Knowledge Mining

### 2.1. Information Sources

Knowledge mining is a series of processing processes that extract potential useful knowledge hidden in a large number of incomplete, fuzzy and random practical application data, which displays them in the final understandable mode [11]. The construction of the system mainly includes the following four aspects:(1)
Sort Out the Context of Enterprise Knowledge. The system classifies and stores all kinds of information according to the needs of enterprises, individuals, and various professional subjects, to establish an enterprise organization knowledge base, a personal knowledge base, a professional special knowledge base, and other knowledge bases.(2)
Deepen Enterprise Knowledge Mining. The system provides semantic retrieval for all kinds of knowledge bases established by enterprises. In the retrieval, according to the similarity of knowledge content, it is aggregated into different categories, and the precise category subject words are given for each aggregated category.(3)
Provide All Kinds of Knowledge Services. The system realizes the sharing of information resources among users, and makes objective evaluation for the information they use.


### 2.2. Function Design

According to the analysis of the system construction content, the main functions of the system include four modules: knowledge context structure, knowledge mining, knowledge analysis, and knowledge service. The main function structure is shown in Figure1.Figure 1
System function structure.Knowledge context: the main function is the management and structure of personal and organizational information as well as the construction of knowledge systems, which not only defines the main structure of the knowledge system according to the structured information of individuals and organizations but also injects various subject databases and information from the network into the knowledge system according to the structure to form a complete knowledge system. The main structure of a knowledge system can be modified and defined manually to help the system build a more reasonable structure.In knowledge mining, the main function is to provide word meaning retrieval, intelligent indexing, and fuzzy classification of word meaning, and self-construction of semantic lexicon. Knowledge service: the main function is to provide common knowledge service on the basis of a knowledge system, including knowledge evaluation, knowledge sharing, user personalized customization and push, document related recommendation, writing assistance, autonomous learning, and other specific functions.Knowledge analysis: the main function is to conduct in-depth analysis and mining on the basis of knowledge systems, which provide special knowledge services, such as academic trend analysis, attention analysis, academic association analysis of institutional personnel, and so on.


### 2.3. Operation Process

Figure2 shows the running process of mining system function. The core process includes the following three steps:(1)
Request Data and Preprocess Data According to Mining Requirements. First of all, the data used in mining must be obtained after defining the mining requirements. To get the requested data, we need to do some targeted preprocessing, then, store it in the data warehouse for later matching to the appropriate model and final call by the corresponding algorithm.(2)
Model and Algorithm Matching and Training. In this step, we can find the best matching method according to the requirements of the mining algorithm.(3)
Prediction and Evaluation of Results. According to the prediction results and the results of mining, whether the results meet the mining needs is evaluated. If the requirements have not been met, the mining requirements need to be further modified or algorithm training should be carried out; if the requirements are met, they can be stored and published.Figure 2
System operation process.


### 2.4. Platform Construction

Through the research of system dynamic configuration strategy and mode, function dynamic configuration loading method, unified hierarchical parameter configuration method, function component (module) registration method, service release, and registration mechanism. It provides technical support and guarantees for the platform’s ability to open interaction and compatible expansion.In terms of rule-making and mechanism guarantee, this paper first studies the fast switching mechanism to support normal state and high concurrency and high load state. It also studies load-aware performance bottleneck location, preemptive scheduling, adaptive optimization, and so on. The dynamic scheduling of resources and tasks is realized to meet the highly flexible dynamic expansion of the platform. Second, the unified resource management, dynamic adjustment, and migration backup mechanisms are studied. The technologies of load balancing, dynamic resource scheduling, real-time status monitoring, high fault tolerance self-healing, intelligent diagnosis, and maintenance are studied to provide technical support for the comprehensive management of the platform, support the high availability of the platform, it enhance the robustness and stability of the platform.In addition, according to the requirements of data security, application security, and maintenance security in the integration and sharing of enterprise R&D knowledge resources, a multidimensional security system model of the platform is constructed to comprehensively protect the physical infrastructure, virtualization network, system, application, data, etc. So as to meet the requirements of risk control, compliance, and reliability, we built unified security architecture to solve security management, security protection, and security compliance issues.


## 2.1. Information Sources

Knowledge mining is a series of processing processes that extract potential useful knowledge hidden in a large number of incomplete, fuzzy and random practical application data, which displays them in the final understandable mode [11]. The construction of the system mainly includes the following four aspects:(1)
Sort Out the Context of Enterprise Knowledge. The system classifies and stores all kinds of information according to the needs of enterprises, individuals, and various professional subjects, to establish an enterprise organization knowledge base, a personal knowledge base, a professional special knowledge base, and other knowledge bases.(2)
Deepen Enterprise Knowledge Mining. The system provides semantic retrieval for all kinds of knowledge bases established by enterprises. In the retrieval, according to the similarity of knowledge content, it is aggregated into different categories, and the precise category subject words are given for each aggregated category.(3)
Provide All Kinds of Knowledge Services. The system realizes the sharing of information resources among users, and makes objective evaluation for the information they use.


## 2.2. Function Design

According to the analysis of the system construction content, the main functions of the system include four modules: knowledge context structure, knowledge mining, knowledge analysis, and knowledge service. The main function structure is shown in Figure1.Figure 1
System function structure.Knowledge context: the main function is the management and structure of personal and organizational information as well as the construction of knowledge systems, which not only defines the main structure of the knowledge system according to the structured information of individuals and organizations but also injects various subject databases and information from the network into the knowledge system according to the structure to form a complete knowledge system. The main structure of a knowledge system can be modified and defined manually to help the system build a more reasonable structure.In knowledge mining, the main function is to provide word meaning retrieval, intelligent indexing, and fuzzy classification of word meaning, and self-construction of semantic lexicon. Knowledge service: the main function is to provide common knowledge service on the basis of a knowledge system, including knowledge evaluation, knowledge sharing, user personalized customization and push, document related recommendation, writing assistance, autonomous learning, and other specific functions.Knowledge analysis: the main function is to conduct in-depth analysis and mining on the basis of knowledge systems, which provide special knowledge services, such as academic trend analysis, attention analysis, academic association analysis of institutional personnel, and so on.


## 2.3. Operation Process

Figure2 shows the running process of mining system function. The core process includes the following three steps:(1)
Request Data and Preprocess Data According to Mining Requirements. First of all, the data used in mining must be obtained after defining the mining requirements. To get the requested data, we need to do some targeted preprocessing, then, store it in the data warehouse for later matching to the appropriate model and final call by the corresponding algorithm.(2)
Model and Algorithm Matching and Training. In this step, we can find the best matching method according to the requirements of the mining algorithm.(3)
Prediction and Evaluation of Results. According to the prediction results and the results of mining, whether the results meet the mining needs is evaluated. If the requirements have not been met, the mining requirements need to be further modified or algorithm training should be carried out; if the requirements are met, they can be stored and published.Figure 2
System operation process.


## 2.4. Platform Construction

Through the research of system dynamic configuration strategy and mode, function dynamic configuration loading method, unified hierarchical parameter configuration method, function component (module) registration method, service release, and registration mechanism. It provides technical support and guarantees for the platform’s ability to open interaction and compatible expansion.In terms of rule-making and mechanism guarantee, this paper first studies the fast switching mechanism to support normal state and high concurrency and high load state. It also studies load-aware performance bottleneck location, preemptive scheduling, adaptive optimization, and so on. The dynamic scheduling of resources and tasks is realized to meet the highly flexible dynamic expansion of the platform. Second, the unified resource management, dynamic adjustment, and migration backup mechanisms are studied. The technologies of load balancing, dynamic resource scheduling, real-time status monitoring, high fault tolerance self-healing, intelligent diagnosis, and maintenance are studied to provide technical support for the comprehensive management of the platform, support the high availability of the platform, it enhance the robustness and stability of the platform.In addition, according to the requirements of data security, application security, and maintenance security in the integration and sharing of enterprise R&D knowledge resources, a multidimensional security system model of the platform is constructed to comprehensively protect the physical infrastructure, virtualization network, system, application, data, etc. So as to meet the requirements of risk control, compliance, and reliability, we built unified security architecture to solve security management, security protection, and security compliance issues.


## 3. Enterprise Data Management Model Based on Improved Decision Tree Algorithm

As shown in Figure3, the tasks are planned and decomposed based on the platform, where the discrete task activity nodes are assigned to multiple enterprises for collaborative completion. At the same time, the resource allocation is strictly controlled by the platform. For different activity nodes, search in the knowledge base to obtain the corresponding feasible set of knowledge resources, then, the arrangement and combination scheme of knowledge resources can be obtained. When the types of knowledge resources are more abundant or the number of activities is larger, the combination number will be difficult to estimate, and the difficulty of resource matching will rise sharply [12]. Therefore, by clustering knowledge resources, the retrieval process of feasible sets of resources is simplified. Using the knowledge data of a knowledge base, integrated data of a data warehouse, and engineering construction data generated by business applications, the analysis and prediction model based on decision classification can provide an important basis for enterprises’ intelligent decision-making and gain advantages in competition.Figure 3
Enterprise knowledge resource management process.


### 3.1. C4.5 Algorithm

Aiming at the problem of reducing classification accuracy caused by redundancy of feature attributes in enterprise classification data sets, an improved decision tree algorithm is proposed in this paper. The method optimizes the selection of the best threshold segmentation point in the process of discretizing continuous value feature attributes, and modifies the calculation method of information gain rate.First, entropyHX,a is used to represent the cost of data set classification to obtain attribute fielda, which is defined as follows:(1)HX,a=−∑i=1kninbnin.Among them,k is the number of values of attribute a, and n is the total number of samples. Then, the information gain ratio is used to represent the amount of information obtained per unit cost, which is defined as follows:(2)EX,a=IX,aHX,a,where IX,a is the information gain, and the information gain of each attribute is compared to a calculation to determine the test attribute field.The traditional C4.5 algorithm processes continuous values as follows:(1)
The variables in the data sample subset are sorted uniformly, and the attribute sequence is obtained in ascending orderA1,A2,…,An.(2)
According to the partition of attribute values, then−1 candidate segmentation threshold point is obtained. For the i segmentation threshold point, thesegmentation value is set to Si= middle Ai,Ai+1=Ai,Ai+1/2, and the sample set is divided into two subsets.(3)
There aren−1 candidate threshold segmentation points, and the information gain rate coefficient of threshold segmentation point is calculated. The best threshold segmentation point is the point with the largest calculation coefficient.In the aforementioned calculation, it is necessary to calculate the information gain rate ofn−1 threshold segmentation points. When the total amount of data is large, the problem of high time complexity is easy to appear. In view of the above problems, in C4.5 decision tree processing of continuous variable splitting attribute, a method of optimizing threshold segmentation points is proposed, which reduces the division of segmentation threshold points and reduces the time complexity of the algorithm. When the data set sample is large, it can improve the operation efficiency.


### 3.2. Attribute Reduction

The Pearson coefficient in probability theory is used to measure the correlation between attributes [13]. The goal of attribute reduction is: for decision attributes and feature attributes, the greater the correlation between them is, the better, so as to ensure that there are no other irrelevant attributes in the feature attribute subset; for feature attributes, the smaller the correlation between each attribute, the better, so that there are no redundant attributes in the attribute subset.The correlation coefficient (Pearson) between random variablesX,Y is expressed as follows:(3)ρxy=CovX,YDXDY.Between two arbitrary random variables, the following equation holds:(4)DX+Y=DX+DY+2CovX,Y,CovX,Y=EXY−EXEY.In the decision tree model,X,Y represents two characteristic attribute sets in the data set, the total number of data samples is num, and their attribute values are, respectively, X=X1,X2,…,Xm, including attribute values ofm; Y=Y1,Y2,…,Yn, including attribute values of n. Where Xi represents the value of i in feature attributeX, Yj represents the value of j in feature attributeY. nai represents the number of samples that meet condition X=Xi, nbj represents the number of samples of Y=Yj, and naibj represents the number of samples of X=Xi and Y=Yj.The correlation coefficientρxy of characteristic attribute X,Y takes the absolute value, which can be deduced as follows:(5)ρxy=EXY−EXEYEX2−EX2−EY2−EX2.For the feature attributes existing in the dataset, the greater the correlation between them, the greater the value ofp, otherwise the smaller. When the correlation between attributes is large, by comparing the information gain rate between attributes, the redundant attributes with small information gain rate can be removed to reduce the characteristic attributes of data set and improve the accuracy of decision tree model.


### 3.3. Optimal Judgment of Segmentation Point

Based on the boundary point determination theorem, the time complexity of the algorithm is reduced by dividing the boundary points. The steps are as follows.(1)
The continuous attributes are sorted in ascending order to get the sequence.(2)
The threshold segmentation points are divided, and whether each threshold segmentation point belongs to the boundary point is determined.(3)
The information gain of boundary points is calculated, and the boundary points with the largest information gain are selected for discretization.(4)
The continuous attribute values are divided into two parts, and the decision tree nodes are constructed.


### 3.4. Model Implementation

The Pseudocode of C4.5 decision tree after optimization is as follows:Input: nodeN, training sample set S, classification attribute set A;Output: a decision tree; procedure make_decision_tree (N,S,A);Initialize the root node;A is calculated in the sample set A, and the branch scheme of A is solved; if (A is a continuous attribute).The threshold segmentation points are obtained in ascending order;If (threshold segmentation point is boundary point In).The gain coefficient of In was calculated;Select Max_In discretization; if (nodeN satisfies the branching condition).S is divided into S1 and S2 by branch scheme; create subnodes N1 and N2 of N; make_decision_tree (N1,S1,A);make_decision_tree (N2,S2,A);endif endWhen determining the best threshold segmentation point, the partial continuous value attribute is selected to avoid the differentiation of the feature attribute, and the method of processing the continuous value feature attribute is modified.Step 1 Sort the consecutive attribute values on the node in ascending order to obtain the attribute sequenceA1,A2,…,An,…,Atotal.Step 2 In ascending order, total-1 threshold segmentation points are generated, where the value of the NTH segmentation point isAn+An+1/2. The sample data set is divided into two subsets, and boundary points are divided by boundary point optimization method to reduce the calculation of threshold segmentation points.Step 3 Calculate the information gain of boundary points, and select the boundary point with the maximum information gain as the best segmentation point of the feature attribute.Step 4 discretize the continuous valued attributes, and calculate the best boundary point of information gain rate, by subtracting the lb (N − 1)/|D| is, it has been modified, and the correction method is(6)GainRatio=GainRatioX−lbN−1lendataset,whereN is the number of values of continuous features, D is the sample size of training dataset, and Gain Ratio x is the information gain rate after discretization of attribute x of this node.


## 3.1. C4.5 Algorithm

Aiming at the problem of reducing classification accuracy caused by redundancy of feature attributes in enterprise classification data sets, an improved decision tree algorithm is proposed in this paper. The method optimizes the selection of the best threshold segmentation point in the process of discretizing continuous value feature attributes, and modifies the calculation method of information gain rate.First, entropyHX,a is used to represent the cost of data set classification to obtain attribute fielda, which is defined as follows:(1)HX,a=−∑i=1kninbnin.Among them,k is the number of values of attribute a, and n is the total number of samples. Then, the information gain ratio is used to represent the amount of information obtained per unit cost, which is defined as follows:(2)EX,a=IX,aHX,a,where IX,a is the information gain, and the information gain of each attribute is compared to a calculation to determine the test attribute field.The traditional C4.5 algorithm processes continuous values as follows:(1)
The variables in the data sample subset are sorted uniformly, and the attribute sequence is obtained in ascending orderA1,A2,…,An.(2)
According to the partition of attribute values, then−1 candidate segmentation threshold point is obtained. For the i segmentation threshold point, thesegmentation value is set to Si= middle Ai,Ai+1=Ai,Ai+1/2, and the sample set is divided into two subsets.(3)
There aren−1 candidate threshold segmentation points, and the information gain rate coefficient of threshold segmentation point is calculated. The best threshold segmentation point is the point with the largest calculation coefficient.In the aforementioned calculation, it is necessary to calculate the information gain rate ofn−1 threshold segmentation points. When the total amount of data is large, the problem of high time complexity is easy to appear. In view of the above problems, in C4.5 decision tree processing of continuous variable splitting attribute, a method of optimizing threshold segmentation points is proposed, which reduces the division of segmentation threshold points and reduces the time complexity of the algorithm. When the data set sample is large, it can improve the operation efficiency.


## 3.2. Attribute Reduction

The Pearson coefficient in probability theory is used to measure the correlation between attributes [13]. The goal of attribute reduction is: for decision attributes and feature attributes, the greater the correlation between them is, the better, so as to ensure that there are no other irrelevant attributes in the feature attribute subset; for feature attributes, the smaller the correlation between each attribute, the better, so that there are no redundant attributes in the attribute subset.The correlation coefficient (Pearson) between random variablesX,Y is expressed as follows:(3)ρxy=CovX,YDXDY.Between two arbitrary random variables, the following equation holds:(4)DX+Y=DX+DY+2CovX,Y,CovX,Y=EXY−EXEY.In the decision tree model,X,Y represents two characteristic attribute sets in the data set, the total number of data samples is num, and their attribute values are, respectively, X=X1,X2,…,Xm, including attribute values ofm; Y=Y1,Y2,…,Yn, including attribute values of n. Where Xi represents the value of i in feature attributeX, Yj represents the value of j in feature attributeY. nai represents the number of samples that meet condition X=Xi, nbj represents the number of samples of Y=Yj, and naibj represents the number of samples of X=Xi and Y=Yj.The correlation coefficientρxy of characteristic attribute X,Y takes the absolute value, which can be deduced as follows:(5)ρxy=EXY−EXEYEX2−EX2−EY2−EX2.For the feature attributes existing in the dataset, the greater the correlation between them, the greater the value ofp, otherwise the smaller. When the correlation between attributes is large, by comparing the information gain rate between attributes, the redundant attributes with small information gain rate can be removed to reduce the characteristic attributes of data set and improve the accuracy of decision tree model.


## 3.3. Optimal Judgment of Segmentation Point

Based on the boundary point determination theorem, the time complexity of the algorithm is reduced by dividing the boundary points. The steps are as follows.(1)
The continuous attributes are sorted in ascending order to get the sequence.(2)
The threshold segmentation points are divided, and whether each threshold segmentation point belongs to the boundary point is determined.(3)
The information gain of boundary points is calculated, and the boundary points with the largest information gain are selected for discretization.(4)
The continuous attribute values are divided into two parts, and the decision tree nodes are constructed.


## 3.4. Model Implementation

The Pseudocode of C4.5 decision tree after optimization is as follows:Input: nodeN, training sample set S, classification attribute set A;Output: a decision tree; procedure make_decision_tree (N,S,A);Initialize the root node;A is calculated in the sample set A, and the branch scheme of A is solved; if (A is a continuous attribute).The threshold segmentation points are obtained in ascending order;If (threshold segmentation point is boundary point In).The gain coefficient of In was calculated;Select Max_In discretization; if (nodeN satisfies the branching condition).S is divided into S1 and S2 by branch scheme; create subnodes N1 and N2 of N; make_decision_tree (N1,S1,A);make_decision_tree (N2,S2,A);endif endWhen determining the best threshold segmentation point, the partial continuous value attribute is selected to avoid the differentiation of the feature attribute, and the method of processing the continuous value feature attribute is modified.Step 1 Sort the consecutive attribute values on the node in ascending order to obtain the attribute sequenceA1,A2,…,An,…,Atotal.Step 2 In ascending order, total-1 threshold segmentation points are generated, where the value of the NTH segmentation point isAn+An+1/2. The sample data set is divided into two subsets, and boundary points are divided by boundary point optimization method to reduce the calculation of threshold segmentation points.Step 3 Calculate the information gain of boundary points, and select the boundary point with the maximum information gain as the best segmentation point of the feature attribute.Step 4 discretize the continuous valued attributes, and calculate the best boundary point of information gain rate, by subtracting the lb (N − 1)/|D| is, it has been modified, and the correction method is(6)GainRatio=GainRatioX−lbN−1lendataset,whereN is the number of values of continuous features, D is the sample size of training dataset, and Gain Ratio x is the information gain rate after discretization of attribute x of this node.


## 4. Experimental Results and Analysis

### 4.1. Parameter Settings

The experiment is carried out on the PyCharm platform, and the algorithm is implemented in the Python language. The data set used in this paper is from the UCI machine learning platform. The abalone dataset is used to verify the model, and its feature attributes include continuous value attributes and discrete value attributes. The number of attributes is 39, and the number of instances is 898.The CPU running time and accuracy are used as the evaluation criteria to measure the time complexity and accuracy of the algorithm. The test case is to generate a classification decision tree. First, the feature attributes of the data set are reduced to remove the redundant attributes. Then, the discrete continuous value feature attributes are optimized, where the information gain rate calculation is modified.


### 4.2. Comparison of Different Models

In the experiment, 70% of the data set is randomly selected as the training set and 30% as the test set. The average value of the experiment is taken as the experimental result, as shown in Figures4 and 5.Figure 4
Running time of different models.Figure 5
Classification accuracy of different models.The experimental results show that the improved C4.5 algorithm has a certain improvement in time-running efficiency and model accuracy. The boundary decision optimization based on threshold segmentation points can reduce the computation of continuous value attribute discretization and improve the efficiency of decision tree generation. When the sample size is small, the operation efficiency is improved by about 50%. With the increase of the number of samples, the improvement of operation efficiency is more obvious. At the same time, the reduction of redundant attributes and the modification of information gain rate can effectively improve the accuracy of model classification.


### 4.3. Enterprise Data Management

Through the analysis of variable characteristics and redundancy of feature attributes, the relationship between decision attributes and decision dependent variables can be preliminarily explored. The system provides a visual display of the relationship between variables and analyzes the relationship between the two. It provides a decision-making reference for fine management of enterprises. Taking employee turnover as an example, the visual data management results are shown in Figures6 and 7.Figure 6
Relationship between satisfaction and turnover factors.Figure 7
Relationship between working years and turnover factors.As can be seen from the visual display chart, in the company departments, the sales department has the greatest influence on whether to leave the company or not, so that the proportion of staff leaving the post is the highest. In the relationship between salary levels and turnover factors, employees with lower salary levels tend to leave, so that the influencing factors far exceed the medium and high levels. The turnover rate of employees with higher salary levels is lower, which is in line with the current situation of personnel flow in enterprises. The satisfaction level of employees is mainly in the range of 0.6–1.0. The higher the degree of satisfaction, the lower the probability of turnover and the greater the possibility of staying in the enterprise; in the relationship between working years and turnover factors, the stability of employees who have worked in enterprises for 3 years is higher. With the increase of working years, the turnover tendency of employees increases. Therefore, in the enterprise management, for the employees with long working years, the enterprise should pay more attention to the working hours, salary level, and whether the satisfaction of the enterprise is within a reasonable range. So as to carry out reasonable and scientific standardized management of enterprise personnel this also can reduce staff turnover.


## 4.1. Parameter Settings

The experiment is carried out on the PyCharm platform, and the algorithm is implemented in the Python language. The data set used in this paper is from the UCI machine learning platform. The abalone dataset is used to verify the model, and its feature attributes include continuous value attributes and discrete value attributes. The number of attributes is 39, and the number of instances is 898.The CPU running time and accuracy are used as the evaluation criteria to measure the time complexity and accuracy of the algorithm. The test case is to generate a classification decision tree. First, the feature attributes of the data set are reduced to remove the redundant attributes. Then, the discrete continuous value feature attributes are optimized, where the information gain rate calculation is modified.


## 4.2. Comparison of Different Models

In the experiment, 70% of the data set is randomly selected as the training set and 30% as the test set. The average value of the experiment is taken as the experimental result, as shown in Figures4 and 5.Figure 4
Running time of different models.Figure 5
Classification accuracy of different models.The experimental results show that the improved C4.5 algorithm has a certain improvement in time-running efficiency and model accuracy. The boundary decision optimization based on threshold segmentation points can reduce the computation of continuous value attribute discretization and improve the efficiency of decision tree generation. When the sample size is small, the operation efficiency is improved by about 50%. With the increase of the number of samples, the improvement of operation efficiency is more obvious. At the same time, the reduction of redundant attributes and the modification of information gain rate can effectively improve the accuracy of model classification.


## 4.3. Enterprise Data Management

Through the analysis of variable characteristics and redundancy of feature attributes, the relationship between decision attributes and decision dependent variables can be preliminarily explored. The system provides a visual display of the relationship between variables and analyzes the relationship between the two. It provides a decision-making reference for fine management of enterprises. Taking employee turnover as an example, the visual data management results are shown in Figures6 and 7.Figure 6
Relationship between satisfaction and turnover factors.Figure 7
Relationship between working years and turnover factors.As can be seen from the visual display chart, in the company departments, the sales department has the greatest influence on whether to leave the company or not, so that the proportion of staff leaving the post is the highest. In the relationship between salary levels and turnover factors, employees with lower salary levels tend to leave, so that the influencing factors far exceed the medium and high levels. The turnover rate of employees with higher salary levels is lower, which is in line with the current situation of personnel flow in enterprises. The satisfaction level of employees is mainly in the range of 0.6–1.0. The higher the degree of satisfaction, the lower the probability of turnover and the greater the possibility of staying in the enterprise; in the relationship between working years and turnover factors, the stability of employees who have worked in enterprises for 3 years is higher. With the increase of working years, the turnover tendency of employees increases. Therefore, in the enterprise management, for the employees with long working years, the enterprise should pay more attention to the working hours, salary level, and whether the satisfaction of the enterprise is within a reasonable range. So as to carry out reasonable and scientific standardized management of enterprise personnel this also can reduce staff turnover.


## 5. Conclusions

This paper introduces the method and model of data mining into knowledge transfer and combines qualitative analysis of knowledge transfer with quantitative simulation of knowledge mining. It designs enterprise data management systems based on knowledge mining, which is more accurate and efficient for multisource information processing, and provides an information resource database. With access to huge information resources and information processing tools, enterprises can make scientific decisions quickly and timely. The lower cost also ensures the operation and subsequent development of the system. The system takes the system module as the core, collects, and analyzes the feedback information from the surrounding multisystem, realizes the multiparty collaborative cluster decision-making and efficient real-time control decision-making, so it can provide an interactive decision-making processing platform for enterprise management.



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*Source: 6359293-2022-09-29.xml*