# Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

Authors: Xiaorong Li; Zhinian Shu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to break through the limitations of current clustering algorithms and avoid the direct impact of disturbance on the clustering effect of abnormal big data texts, a big data text clustering algorithm based on swarm intelligence is proposed. According to the characteristics of swarm intelligence, a differential privacy protection model is constructed; At the same time, the data location information operation is carried out to complete the location information preprocessing. Based on KD tree division, the allocation of the differential privacy budget is completed, and the location information is clustered by dimension reduction. Based on the result of the location information dimension reduction clustering, a differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. Select the fuzzy confidence and support thresholds to extract the associated features, use the features to determine the big data text targets to be clustered, establish a target decision matrix, determine the clustering targets, and obtain the target weights, the obtained membership, and the decision results. Realize the effective clustering of big data text based on swarm intelligence. The experimental results show that the algorithm can effectively realize the high-efficiency clustering of big data texts, and the clustering time is short, and it has a good clustering effect.


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## Body

## 1. Introduction

The continuous development of computer technology has gradually moved towards a comprehensive digital era, and people begin to apply digital technology in various fields. Therefore, it is necessary to study methods that can deal with big data efficiently and solve problems such as data intelligence [1–3]. In order to distinguish different types of big data texts, swarm intelligence big data is divided according to the information structure to provide a flexible data conversion platform for the computer system [4]. Due to the incomplete information structure of big data, it is necessary to constantly modify the data structure and then complete data query, data collection, data analysis, and text clustering according to the characteristics of big data. Big data text clustering is a more complex basic work. Only by completing big data analysis from multiple perspectives and discovering potential data in time can we stably support the operation of computer system [5, 6].Relevant scholars have studied this. Reference [7] proposes an adaptive heuristic clustering algorithm to maximize the impact in complex networks. Firstly, a path-based node similarity measurement method, namely, dynamic local similarity index, is proposed, which can be dynamically adjusted to the optimal mode according to the network topology characteristics. Compared with Katz index with high complexity and LP index with limited application range, this index achieves a good balance between complexity and accuracy. Secondly, combining extended neighborhood kernel with minimum distance, a new clustering initial center selection strategy is proposed, which helps to accelerate the convergence speed of clustering and avoid local optimization in unconnected networks. Finally, an adaptive heuristic clustering algorithm is proposed to find the seed set with the greatest collective influence. Reference [8] proposed an improved s-distance spectrum clustering algorithm. Calculating and monitoring customer churn indicators is very important for companies to retain customers and earn more profits in business. In this study, a customer churn prediction framework is developed through improved spectral clustering. Similarity measurement plays an important role in clustering. By analyzing industrial data, we can more accurately predict customer churn. The linear Euclidean distance in traditional SC is replaced by nonlinear s-distance. Four comprehensive data, eight UCIS, two industrial databases, and a telecom database related to customer churn are analyzed to support the proposed clustering algorithm. Although the above methods have made some progress, the calculation time complexity is high, which will prolong the clustering time of clustering calculation. In order to solve the above problems, a big data text clustering algorithm based on swarm intelligence is proposed. According to the characteristics of swarm intelligence, a differential privacy protection model is constructed. The allocation of differential privacy budget is completed based on KD tree partition, and the location information is clustered by dimensionality reduction. Based on the results of dimensionality reduction clustering of location information, a differential privacy clustering algorithm is proposed. Establish the target decision matrix, determine the clustering target, obtain the target weight, obtain the membership degree, and obtain the decision result. The research shows that the algorithm can effectively realize the efficient clustering of big data texts. The innovation of the designed algorithm is to put forward the characteristics of swarm intelligence. Swarm intelligence control is distributed, and there is no central control. Each individual in the group can change the environment and has the characteristics of simplicity and self-organization.


## 2. Research on Big Data Text Clustering Algorithm under Swarm Intelligence

### 2.1. Construction of Differential Privacy Protection Model

Swarm intelligence is a widely used artificial intelligence technology at this stage. With the development of this technology, it has been gradually applied to social, economic, and other fields [9]. Swarm intelligence comes from the research on the swarm behavior of social insects represented by ant algorithm and bee algorithm. Swarm intelligence has the following characteristics:
(1)
Swarm intelligence control is distributed, and there is no central control [10]. Therefore, it can better adapt to the working state in the current network environment and has strong robustness; that is, it will not affect the group’s solution to the whole problem due to the failure of one or several individuals(2)
Each individual in the group can change the environment, which is a way of indirect communication between individuals, which is called “stimulating work.” Since swarm intelligence can transmit and cooperate information through indirect communication, with the increase of the number of individuals, the increase of communication overhead is small, so it has good scalability [11](3)
The ability or behavior rules of each individual in the group are very simple, so the implementation of swarm intelligence is more convenient and simple(4)
The complex behavior shown by the group is the intelligence emerging through the interaction process of simple individuals. Therefore, the group has self-organizationUnder swarm intelligence, big data text has the characteristics of large-scale and real-time update, in which location information is the main form, and location information in different forms will fuse with each other, resulting in confusion of location information attributes and tuples. Therefore, a differential privacy protection model is constructed to provide better guarantee for the security of location information [12, 13].Differential privacy technology is independent of background knowledge attacks. It can ensure the security of location information on the premise of ensuring the characteristics and attributes of data. It has been applied in many fields, such as big data privacy and big data distribution [14]. Differential privacy technology is introduced into big data text clustering algorithm, which can maximize the differential differentiation of clustering results and obtain higher security [15, 16].The framework of differential privacy protection model is shown in Figure1.Figure 1
Framework of differential privacy protection model.As shown in Figure1, the user location information collected by mobile sensors and other devices is loaded into the Internet of Things data for storage. The user queries or extracts the location information through the query function, and the returned result is the noise disturbance information processed by differential privacy technology [17].


### 2.2. Location Information Preprocessing

In big data text, location information is one of the key information in users’ personal information [18]. Location information mainly includes three factors: space, time, and behavior. Location information is expressed as a collection of geographic information, time information, and behavior trajectory, which has heterogeneous and complex characteristics. Therefore, preprocess the location information, reduce the dimension of the mixed location information and map it to the plane coordinate axis, optimize the time, and realize the digitization of location information [19, 20]. The optimized time structure is shown in Figure 2.Figure 2
Optimization time structure.According to Figure2, Tm represents the optimization time and S1 represents the optimization distance. Assuming that the coordinate of the moving target Oo at t time is x1,y1, its position information is expressed as
(1)Pp=Oox1,y1t×Wwx1,y1t.In formula (1), Ww represents the behavior track, which is continuously updated. According to the location information of the moving target sampled at a certain time interval, the time series is as follows:
(2)Tt=x1,y1t1,x2,y2t2,⋯,xn,yntn.In formula (2), xn,yntn represents the position of the moving target at time tn. In the application of the Internet of Things, most behavior trajectories are not synchronized. In order to facilitate calculation, behavior trajectories are synchronized. The distance between two synchronization tracks is
(3)DdistTA,TB=∑i=1nxAi−xBi2+yAi−yBi2tn−t1.In formula (3), DdistTA,TB represents the distance between behavior trajectories TA and TB, n represents the total number of behavior trajectories, xAi and yAi represent the abscissa and ordinate of the position of TA, xBi and yBi represent the abscissa and ordinate of the position of TB, tn represents the n sampling time, and t1 represents the first sampling time.Location information preprocessing can reduce the operation intensity of differential privacy clustering algorithm [21, 22]. Preprocessing is to digitize location information, which is mainly divided into three steps:Step 1.
Identification of starting and ending points of behavior trajectory [23]. Set the sampling duration threshold. If the residence time of the moving target exceeds the threshold Rt, the sampling will be terminated. This position is the end point of the behavior track and the starting point of the next sampling.Step 2.
Construction of behavior trajectory equivalence class. The behavior trajectory data with similar start and end sampling time are classified into the same equivalent class.Step 3.
If the behavior trackTA and TB belong to the same equivalence class, then
(4)TAta,TBta∈ta1,ta2,TAtb,TBtb∈tb1,tb2.
In formula (4), ta1,ta2 and tb1,tb2 represent the sampling time point range, and ta and tb represent the actual sampling time coefficient, which should be set as required according to the actual situation [24]. Trajectory graph construction refers to the integration of behavior trajectories with synchronous time periods into a unified trajectory graph, which greatly saves computing resources and prepares for the following differential privacy budget allocation.


### 2.3. Differential Privacy Budget Allocation

The key to the implementation of differential privacy protection is to add noise to the location information to distort the real location information and resist similarity attacks. The amount of noise added is determined by the privacy budget parameter, which represents the degree of privacy protection [25]. If the amount of noise added is too small to meet the privacy needs of location information and if the noise is added too much, the availability of location information data will be lost. Therefore, it is necessary to reasonably allocate the differential privacy budget to achieve the best privacy protection effect [26]. The location information data of a given user is shown in Table 1.Table 1
User location information data sheet.
NamexyBob0.9 → 01.8 → 1Alice1.0 → 10.7 → 0Jimmy1.6 → 11.5 → 1Lucy0.5 → 01.2 → 1Tom1.3 → 11.8 → 1Based on Table1, the location information data is divided by KD tree, and the information is denoised to meet the differential privacy protection. The steps of differential privacy budget allocation based on KD tree partition are as follows:Step 1.
Calculate the number of KD tree cells. The calculation formula is(5)vα=maxRu1.
In formula (5), R represents the size of the location information data set, and u1 represents the constant.Step 2.
The location information data set is divided into coarse-grained, and noise is added to each cell to obtain the disturbed location information.Step 3.
Calculate the number of new KD tree cells. The calculation formula is(6)vβ=N′1−vαu2.
In formula (6), N′ represents the amount of noise added for the first division, and u2 represents a constant.Step 4.
Fine-grained deep division ofD′, adding noise to each cell to obtain the disturbance location information.Step 5.
Repeat Step3 and Step 4 to obtain the location information data set to be distributed that meets the difference factor [27].Step 6.
Make consistency constraints on Step2 and Step 4, and adjust to get the final result.


### 2.4. Set Information Clustering Algorithm

According to the above differential privacy budget allocation results, cluster the location big data text of the Internet of Things [28, 29]. When collecting big data texts, we should follow the principle of fairness, adopt the census method, and sample within the controllable range of swarm intelligence. When the sample is randomly selected from the population, which sample is selected is random, and the deviation between the sample index obtained from the sample and the population index is determined. Qualitative analysis of the Internet of Things location big data text clustering steps are as follows:


#### 2.4.1. Input

The location big data text set of the Internet of Things isDde, the number of segmented data is d, and the number of clusters is e.


#### 2.4.2. Output: Clustering Results

Step 1.
Segmentation of location big data elements. The formula for calculating the number of elements of each subblock unit is(7)Q=Ddek.
In formula (7), k represents the size of the big data set at the location of the Internet of Things.Step 2.
The calculation formula of synchronization track distance of subblock unit is expressed as(8)Yd=∑k=1nSk×xik−xjk.
In formula (8), Sk represents the spatial overlapping weight value of any two behavior trajectories, and xik and xjk represent the position information of the two behavior trajectories at the same time.Step 3.
Solve the algebraic sum of spatial overlapping weight values of each subblock element, and the calculation formula is(9)S=∑d=1kYd.
According to formula (9), the results are arranged in descending order, and the subblock units are intercepted and integrated into a cluster.Step 4.
Calculate the center point of the cluster. The formula is(10)Ni=LiQi×S.
In formula (10), Li represents the linear sum of all elements in the cluster, and Qi represents the sum of coefficients of all elements in the cluster.Step 5.
Calculate the distance between two clusters, solve the average cluster spacing, and compare the cluster distance with the average cluster spacing [30]. If the cluster distance is greater than the average cluster distance, keep the original cluster and return to Step 3 for iteration until the end conditions are met [31]. If the cluster distance is less than the average cluster spacing, they are combined into one cluster.Step 6.
Output the final clustering result.Based on the dimensionality reduction clustering results of the above location information, a clustering algorithm is constructed, and its process is shown in Figure3.Figure 3
Flow chart of clustering algorithm.Through the above process, the research on big data text clustering algorithm based on location information is realized, which provides a more effective guarantee for the security of users’ personal information and helps the development of the Internet of Things.


## 2.1. Construction of Differential Privacy Protection Model

Swarm intelligence is a widely used artificial intelligence technology at this stage. With the development of this technology, it has been gradually applied to social, economic, and other fields [9]. Swarm intelligence comes from the research on the swarm behavior of social insects represented by ant algorithm and bee algorithm. Swarm intelligence has the following characteristics:
(1)
Swarm intelligence control is distributed, and there is no central control [10]. Therefore, it can better adapt to the working state in the current network environment and has strong robustness; that is, it will not affect the group’s solution to the whole problem due to the failure of one or several individuals(2)
Each individual in the group can change the environment, which is a way of indirect communication between individuals, which is called “stimulating work.” Since swarm intelligence can transmit and cooperate information through indirect communication, with the increase of the number of individuals, the increase of communication overhead is small, so it has good scalability [11](3)
The ability or behavior rules of each individual in the group are very simple, so the implementation of swarm intelligence is more convenient and simple(4)
The complex behavior shown by the group is the intelligence emerging through the interaction process of simple individuals. Therefore, the group has self-organizationUnder swarm intelligence, big data text has the characteristics of large-scale and real-time update, in which location information is the main form, and location information in different forms will fuse with each other, resulting in confusion of location information attributes and tuples. Therefore, a differential privacy protection model is constructed to provide better guarantee for the security of location information [12, 13].Differential privacy technology is independent of background knowledge attacks. It can ensure the security of location information on the premise of ensuring the characteristics and attributes of data. It has been applied in many fields, such as big data privacy and big data distribution [14]. Differential privacy technology is introduced into big data text clustering algorithm, which can maximize the differential differentiation of clustering results and obtain higher security [15, 16].The framework of differential privacy protection model is shown in Figure1.Figure 1
Framework of differential privacy protection model.As shown in Figure1, the user location information collected by mobile sensors and other devices is loaded into the Internet of Things data for storage. The user queries or extracts the location information through the query function, and the returned result is the noise disturbance information processed by differential privacy technology [17].


## 2.2. Location Information Preprocessing

In big data text, location information is one of the key information in users’ personal information [18]. Location information mainly includes three factors: space, time, and behavior. Location information is expressed as a collection of geographic information, time information, and behavior trajectory, which has heterogeneous and complex characteristics. Therefore, preprocess the location information, reduce the dimension of the mixed location information and map it to the plane coordinate axis, optimize the time, and realize the digitization of location information [19, 20]. The optimized time structure is shown in Figure 2.Figure 2
Optimization time structure.According to Figure2, Tm represents the optimization time and S1 represents the optimization distance. Assuming that the coordinate of the moving target Oo at t time is x1,y1, its position information is expressed as
(1)Pp=Oox1,y1t×Wwx1,y1t.In formula (1), Ww represents the behavior track, which is continuously updated. According to the location information of the moving target sampled at a certain time interval, the time series is as follows:
(2)Tt=x1,y1t1,x2,y2t2,⋯,xn,yntn.In formula (2), xn,yntn represents the position of the moving target at time tn. In the application of the Internet of Things, most behavior trajectories are not synchronized. In order to facilitate calculation, behavior trajectories are synchronized. The distance between two synchronization tracks is
(3)DdistTA,TB=∑i=1nxAi−xBi2+yAi−yBi2tn−t1.In formula (3), DdistTA,TB represents the distance between behavior trajectories TA and TB, n represents the total number of behavior trajectories, xAi and yAi represent the abscissa and ordinate of the position of TA, xBi and yBi represent the abscissa and ordinate of the position of TB, tn represents the n sampling time, and t1 represents the first sampling time.Location information preprocessing can reduce the operation intensity of differential privacy clustering algorithm [21, 22]. Preprocessing is to digitize location information, which is mainly divided into three steps:Step 1.
Identification of starting and ending points of behavior trajectory [23]. Set the sampling duration threshold. If the residence time of the moving target exceeds the threshold Rt, the sampling will be terminated. This position is the end point of the behavior track and the starting point of the next sampling.Step 2.
Construction of behavior trajectory equivalence class. The behavior trajectory data with similar start and end sampling time are classified into the same equivalent class.Step 3.
If the behavior trackTA and TB belong to the same equivalence class, then
(4)TAta,TBta∈ta1,ta2,TAtb,TBtb∈tb1,tb2.
In formula (4), ta1,ta2 and tb1,tb2 represent the sampling time point range, and ta and tb represent the actual sampling time coefficient, which should be set as required according to the actual situation [24]. Trajectory graph construction refers to the integration of behavior trajectories with synchronous time periods into a unified trajectory graph, which greatly saves computing resources and prepares for the following differential privacy budget allocation.


## 2.3. Differential Privacy Budget Allocation

The key to the implementation of differential privacy protection is to add noise to the location information to distort the real location information and resist similarity attacks. The amount of noise added is determined by the privacy budget parameter, which represents the degree of privacy protection [25]. If the amount of noise added is too small to meet the privacy needs of location information and if the noise is added too much, the availability of location information data will be lost. Therefore, it is necessary to reasonably allocate the differential privacy budget to achieve the best privacy protection effect [26]. The location information data of a given user is shown in Table 1.Table 1
User location information data sheet.
NamexyBob0.9 → 01.8 → 1Alice1.0 → 10.7 → 0Jimmy1.6 → 11.5 → 1Lucy0.5 → 01.2 → 1Tom1.3 → 11.8 → 1Based on Table1, the location information data is divided by KD tree, and the information is denoised to meet the differential privacy protection. The steps of differential privacy budget allocation based on KD tree partition are as follows:Step 1.
Calculate the number of KD tree cells. The calculation formula is(5)vα=maxRu1.
In formula (5), R represents the size of the location information data set, and u1 represents the constant.Step 2.
The location information data set is divided into coarse-grained, and noise is added to each cell to obtain the disturbed location information.Step 3.
Calculate the number of new KD tree cells. The calculation formula is(6)vβ=N′1−vαu2.
In formula (6), N′ represents the amount of noise added for the first division, and u2 represents a constant.Step 4.
Fine-grained deep division ofD′, adding noise to each cell to obtain the disturbance location information.Step 5.
Repeat Step3 and Step 4 to obtain the location information data set to be distributed that meets the difference factor [27].Step 6.
Make consistency constraints on Step2 and Step 4, and adjust to get the final result.


## 2.4. Set Information Clustering Algorithm

According to the above differential privacy budget allocation results, cluster the location big data text of the Internet of Things [28, 29]. When collecting big data texts, we should follow the principle of fairness, adopt the census method, and sample within the controllable range of swarm intelligence. When the sample is randomly selected from the population, which sample is selected is random, and the deviation between the sample index obtained from the sample and the population index is determined. Qualitative analysis of the Internet of Things location big data text clustering steps are as follows:


### 2.4.1. Input

The location big data text set of the Internet of Things isDde, the number of segmented data is d, and the number of clusters is e.


### 2.4.2. Output: Clustering Results

Step 1.
Segmentation of location big data elements. The formula for calculating the number of elements of each subblock unit is(7)Q=Ddek.
In formula (7), k represents the size of the big data set at the location of the Internet of Things.Step 2.
The calculation formula of synchronization track distance of subblock unit is expressed as(8)Yd=∑k=1nSk×xik−xjk.
In formula (8), Sk represents the spatial overlapping weight value of any two behavior trajectories, and xik and xjk represent the position information of the two behavior trajectories at the same time.Step 3.
Solve the algebraic sum of spatial overlapping weight values of each subblock element, and the calculation formula is(9)S=∑d=1kYd.
According to formula (9), the results are arranged in descending order, and the subblock units are intercepted and integrated into a cluster.Step 4.
Calculate the center point of the cluster. The formula is(10)Ni=LiQi×S.
In formula (10), Li represents the linear sum of all elements in the cluster, and Qi represents the sum of coefficients of all elements in the cluster.Step 5.
Calculate the distance between two clusters, solve the average cluster spacing, and compare the cluster distance with the average cluster spacing [30]. If the cluster distance is greater than the average cluster distance, keep the original cluster and return to Step 3 for iteration until the end conditions are met [31]. If the cluster distance is less than the average cluster spacing, they are combined into one cluster.Step 6.
Output the final clustering result.Based on the dimensionality reduction clustering results of the above location information, a clustering algorithm is constructed, and its process is shown in Figure3.Figure 3
Flow chart of clustering algorithm.Through the above process, the research on big data text clustering algorithm based on location information is realized, which provides a more effective guarantee for the security of users’ personal information and helps the development of the Internet of Things.


## 2.4.1. Input

The location big data text set of the Internet of Things isDde, the number of segmented data is d, and the number of clusters is e.


## 2.4.2. Output: Clustering Results

Step 1.
Segmentation of location big data elements. The formula for calculating the number of elements of each subblock unit is(7)Q=Ddek.
In formula (7), k represents the size of the big data set at the location of the Internet of Things.Step 2.
The calculation formula of synchronization track distance of subblock unit is expressed as(8)Yd=∑k=1nSk×xik−xjk.
In formula (8), Sk represents the spatial overlapping weight value of any two behavior trajectories, and xik and xjk represent the position information of the two behavior trajectories at the same time.Step 3.
Solve the algebraic sum of spatial overlapping weight values of each subblock element, and the calculation formula is(9)S=∑d=1kYd.
According to formula (9), the results are arranged in descending order, and the subblock units are intercepted and integrated into a cluster.Step 4.
Calculate the center point of the cluster. The formula is(10)Ni=LiQi×S.
In formula (10), Li represents the linear sum of all elements in the cluster, and Qi represents the sum of coefficients of all elements in the cluster.Step 5.
Calculate the distance between two clusters, solve the average cluster spacing, and compare the cluster distance with the average cluster spacing [30]. If the cluster distance is greater than the average cluster distance, keep the original cluster and return to Step 3 for iteration until the end conditions are met [31]. If the cluster distance is less than the average cluster spacing, they are combined into one cluster.Step 6.
Output the final clustering result.Based on the dimensionality reduction clustering results of the above location information, a clustering algorithm is constructed, and its process is shown in Figure3.Figure 3
Flow chart of clustering algorithm.Through the above process, the research on big data text clustering algorithm based on location information is realized, which provides a more effective guarantee for the security of users’ personal information and helps the development of the Internet of Things.


## 3. Implement the Big Data Clustering Algorithm of the Internet of Things

### 3.1. Networking Event Correlation Characteristics

Big data text has high correlation characteristics, and the massive data formed in the Internet of Things platform has high heterogeneity and mixing. The association attribute between big data texts has nonlinear characteristics [32], which is consistent with the event characteristics of fuzzy theory.Yyand Zz are used to represent the fuzzy confidence and support threshold of the Internet of Things events, respectively, and MAa,Bb is used to represent the correlation characteristics of the Internet of Things events. The gradient descent method is selected to repeatedly iterate the fuzzy confidence and support threshold of the Internet of Things events, make the error ei of the correlation feature between the cloud event Aa and the cloud event Bb in the large data text processed by MAa,Bb close to the support threshold, and make the correlation characteristic distance Dij between cloud event Aa and cloud event Bb tend to be the smallest, which is in the controllable range and close to the same cloud droplet chaotic index set m.
(11)GLH=∑i=1mMAa,BbZz×Yy×Dij×ei.The similarity between Internet of Things eventAa and event Bb is close to m through formula (11), so as to minimize the difference of cloud event correlation characteristics in the Internet of Things. Based on the support threshold of the Internet of Things events, the depth first method is used to cycle the correlation characteristics in cloud mode Internet of Things events, traverse all input parameters in GLH function [33], obtain the best embedding dimension Qi for extracting correlation features, and form a candidate frequent set Hi of correlation features that can reflect the local behavior of cloud mode Internet of Things events. The calculation formula of support threshold is as follows:
(12)LM=GLH×Si×Hi×QiGLH.In formula (12), Hi represents the frequent set of association feature candidates processed by collaborative filtering, Si represents the cloud drop vector processed, and Si is used to obtain the association feature collaborative filtering mapping function of the Internet of Things events in cloud mode. By reconstructing the points with high similarity [34], and the maximum correlation degree, the maximum correlation degree and the points with the maximum similarity of the cluster center points of different event correlation features of the Internet of Things are obtained. The obtained points are used to form cloud droplets, and the correlation features with high similarity are divided into the same clusters to complete the extraction of event correlation features of the Internet of Things.


### 3.2. Big Data Text Clustering for Swarm Intelligence

The running process of big data clustering algorithm to realize swarm intelligence is as follows:


#### 3.2.1. Establish Objective Decision Matrix

Use the extracted event correlation characteristics of the Internet of Things to determine the big data target of the Internet of Things to be clustered, and clarify the clustering target and clustering scheme [35]. f1,f2,⋯,fq and X1,X2,⋯,Xn are, respectively, used to represent the decision objectives and alternatives of the multiobjective decision-making problem of the Internet of Things big data, and the number of decision objectives and alternatives are q and n, respectively. The decision objective matrix of the Internet of Things big data is established and expressed by X=xikn×q; in the decision objective matrix, it represents the value result of scheme Xi1≤i≤n when the objective is fk1≤k≤q.


#### 3.2.2. Determine Target Weight

The target matrixX=xikn×q is represented by the weighted target matrix, where AA, the determined target weight, is expressed in.


#### 3.2.3. Define the Ideal Decision GoalZ+ and the Negative Ideal Decision Goal Z−

The ideal point theory in decision theory is used to clarify the ideal decision goal. In the multiobjective decision analysis of the Internet of Things big data clustering, when there are differences in target attributes, there are differences in the selection of ideal decision objectives and negative ideal decision objectives. When the clustering target of the Internet of Things big data is benefit type or cost type, the larger the target value, the better and the smaller the target value, respectively [36]. When the big data clustering target of the Internet of Things is a fixed value or a fixed interval, it is necessary to set the set target value as a fixed value and a fixed interval to ensure the feasibility of big data clustering of the Internet of Things.


#### 3.2.4. Obtain Decision Results

A labeled data setYb=Z+,Z− is established, which contains ideal decision objectives and negative ideal decision objectives; the sample set to be classified can be obtained by using this data set, which is represented by Y=Yb∪Yu. At this time, Yu is equal to Z. n is used to represent the number of alternative decision-making schemes, and the number of elements in the sample set is s=n+2. When determining the number of samples, the fuzzy partition matrix is obtained by using the fuzzy c-means clustering algorithm. At this time, the alternative scheme can be divided into two categories, and the obtained value can reflect the proximity between the decision-making scheme and the ideal decision-making goal and the negative ideal decision-making goal. The basic flow of big data text clustering algorithm of swarm intelligence is shown in Figure 4.Figure 4
Basic flow chart of big data text clustering algorithm of swarm intelligence.According to Figure4, the ranking results of the alternatives of different IoT big data are obtained by using the ranking of membership degrees obtained by division, and the final decision result is determined to realize the effective clustering of big data texts. The clustering algorithm of swarm intelligence fully considers the decision-making goals of IoT big data to obtain the best clustering effect. In the process of implementing the big data text clustering algorithm, according to the semantic word order characteristics of the text obtained by the above analysis, it is placed in a two-dimensional plane, and an initial area is randomly allocated for each information, and the random form is used to cluster in the grid. Then, the group similarity is calculated, and after the calculation results are obtained, the big data text information of the same attribute is gathered in the same range, thus completing the research on the big data text clustering algorithm based on group intelligence.


## 3.1. Networking Event Correlation Characteristics

Big data text has high correlation characteristics, and the massive data formed in the Internet of Things platform has high heterogeneity and mixing. The association attribute between big data texts has nonlinear characteristics [32], which is consistent with the event characteristics of fuzzy theory.Yyand Zz are used to represent the fuzzy confidence and support threshold of the Internet of Things events, respectively, and MAa,Bb is used to represent the correlation characteristics of the Internet of Things events. The gradient descent method is selected to repeatedly iterate the fuzzy confidence and support threshold of the Internet of Things events, make the error ei of the correlation feature between the cloud event Aa and the cloud event Bb in the large data text processed by MAa,Bb close to the support threshold, and make the correlation characteristic distance Dij between cloud event Aa and cloud event Bb tend to be the smallest, which is in the controllable range and close to the same cloud droplet chaotic index set m.
(11)GLH=∑i=1mMAa,BbZz×Yy×Dij×ei.The similarity between Internet of Things eventAa and event Bb is close to m through formula (11), so as to minimize the difference of cloud event correlation characteristics in the Internet of Things. Based on the support threshold of the Internet of Things events, the depth first method is used to cycle the correlation characteristics in cloud mode Internet of Things events, traverse all input parameters in GLH function [33], obtain the best embedding dimension Qi for extracting correlation features, and form a candidate frequent set Hi of correlation features that can reflect the local behavior of cloud mode Internet of Things events. The calculation formula of support threshold is as follows:
(12)LM=GLH×Si×Hi×QiGLH.In formula (12), Hi represents the frequent set of association feature candidates processed by collaborative filtering, Si represents the cloud drop vector processed, and Si is used to obtain the association feature collaborative filtering mapping function of the Internet of Things events in cloud mode. By reconstructing the points with high similarity [34], and the maximum correlation degree, the maximum correlation degree and the points with the maximum similarity of the cluster center points of different event correlation features of the Internet of Things are obtained. The obtained points are used to form cloud droplets, and the correlation features with high similarity are divided into the same clusters to complete the extraction of event correlation features of the Internet of Things.


## 3.2. Big Data Text Clustering for Swarm Intelligence

The running process of big data clustering algorithm to realize swarm intelligence is as follows:


### 3.2.1. Establish Objective Decision Matrix

Use the extracted event correlation characteristics of the Internet of Things to determine the big data target of the Internet of Things to be clustered, and clarify the clustering target and clustering scheme [35]. f1,f2,⋯,fq and X1,X2,⋯,Xn are, respectively, used to represent the decision objectives and alternatives of the multiobjective decision-making problem of the Internet of Things big data, and the number of decision objectives and alternatives are q and n, respectively. The decision objective matrix of the Internet of Things big data is established and expressed by X=xikn×q; in the decision objective matrix, it represents the value result of scheme Xi1≤i≤n when the objective is fk1≤k≤q.


### 3.2.2. Determine Target Weight

The target matrixX=xikn×q is represented by the weighted target matrix, where AA, the determined target weight, is expressed in.


### 3.2.3. Define the Ideal Decision GoalZ+ and the Negative Ideal Decision Goal Z−

The ideal point theory in decision theory is used to clarify the ideal decision goal. In the multiobjective decision analysis of the Internet of Things big data clustering, when there are differences in target attributes, there are differences in the selection of ideal decision objectives and negative ideal decision objectives. When the clustering target of the Internet of Things big data is benefit type or cost type, the larger the target value, the better and the smaller the target value, respectively [36]. When the big data clustering target of the Internet of Things is a fixed value or a fixed interval, it is necessary to set the set target value as a fixed value and a fixed interval to ensure the feasibility of big data clustering of the Internet of Things.


### 3.2.4. Obtain Decision Results

A labeled data setYb=Z+,Z− is established, which contains ideal decision objectives and negative ideal decision objectives; the sample set to be classified can be obtained by using this data set, which is represented by Y=Yb∪Yu. At this time, Yu is equal to Z. n is used to represent the number of alternative decision-making schemes, and the number of elements in the sample set is s=n+2. When determining the number of samples, the fuzzy partition matrix is obtained by using the fuzzy c-means clustering algorithm. At this time, the alternative scheme can be divided into two categories, and the obtained value can reflect the proximity between the decision-making scheme and the ideal decision-making goal and the negative ideal decision-making goal. The basic flow of big data text clustering algorithm of swarm intelligence is shown in Figure 4.Figure 4
Basic flow chart of big data text clustering algorithm of swarm intelligence.According to Figure4, the ranking results of the alternatives of different IoT big data are obtained by using the ranking of membership degrees obtained by division, and the final decision result is determined to realize the effective clustering of big data texts. The clustering algorithm of swarm intelligence fully considers the decision-making goals of IoT big data to obtain the best clustering effect. In the process of implementing the big data text clustering algorithm, according to the semantic word order characteristics of the text obtained by the above analysis, it is placed in a two-dimensional plane, and an initial area is randomly allocated for each information, and the random form is used to cluster in the grid. Then, the group similarity is calculated, and after the calculation results are obtained, the big data text information of the same attribute is gathered in the same range, thus completing the research on the big data text clustering algorithm based on group intelligence.


## 3.2.1. Establish Objective Decision Matrix

Use the extracted event correlation characteristics of the Internet of Things to determine the big data target of the Internet of Things to be clustered, and clarify the clustering target and clustering scheme [35]. f1,f2,⋯,fq and X1,X2,⋯,Xn are, respectively, used to represent the decision objectives and alternatives of the multiobjective decision-making problem of the Internet of Things big data, and the number of decision objectives and alternatives are q and n, respectively. The decision objective matrix of the Internet of Things big data is established and expressed by X=xikn×q; in the decision objective matrix, it represents the value result of scheme Xi1≤i≤n when the objective is fk1≤k≤q.


## 3.2.2. Determine Target Weight

The target matrixX=xikn×q is represented by the weighted target matrix, where AA, the determined target weight, is expressed in.


## 3.2.3. Define the Ideal Decision GoalZ+ and the Negative Ideal Decision Goal Z−

The ideal point theory in decision theory is used to clarify the ideal decision goal. In the multiobjective decision analysis of the Internet of Things big data clustering, when there are differences in target attributes, there are differences in the selection of ideal decision objectives and negative ideal decision objectives. When the clustering target of the Internet of Things big data is benefit type or cost type, the larger the target value, the better and the smaller the target value, respectively [36]. When the big data clustering target of the Internet of Things is a fixed value or a fixed interval, it is necessary to set the set target value as a fixed value and a fixed interval to ensure the feasibility of big data clustering of the Internet of Things.


## 3.2.4. Obtain Decision Results

A labeled data setYb=Z+,Z− is established, which contains ideal decision objectives and negative ideal decision objectives; the sample set to be classified can be obtained by using this data set, which is represented by Y=Yb∪Yu. At this time, Yu is equal to Z. n is used to represent the number of alternative decision-making schemes, and the number of elements in the sample set is s=n+2. When determining the number of samples, the fuzzy partition matrix is obtained by using the fuzzy c-means clustering algorithm. At this time, the alternative scheme can be divided into two categories, and the obtained value can reflect the proximity between the decision-making scheme and the ideal decision-making goal and the negative ideal decision-making goal. The basic flow of big data text clustering algorithm of swarm intelligence is shown in Figure 4.Figure 4
Basic flow chart of big data text clustering algorithm of swarm intelligence.According to Figure4, the ranking results of the alternatives of different IoT big data are obtained by using the ranking of membership degrees obtained by division, and the final decision result is determined to realize the effective clustering of big data texts. The clustering algorithm of swarm intelligence fully considers the decision-making goals of IoT big data to obtain the best clustering effect. In the process of implementing the big data text clustering algorithm, according to the semantic word order characteristics of the text obtained by the above analysis, it is placed in a two-dimensional plane, and an initial area is randomly allocated for each information, and the random form is used to cluster in the grid. Then, the group similarity is calculated, and after the calculation results are obtained, the big data text information of the same attribute is gathered in the same range, thus completing the research on the big data text clustering algorithm based on group intelligence.


## 4. Experimental Analysis

In order to verify the effectiveness of the big data text clustering algorithm based on swarm intelligence, an experimental test link is set. Six basic functions are selected as the comprehensive performance benchmark function of swarm intelligence. The first two functions have only one peak in each function, and the last four functions have multiple peaks. The multipeak function is used to detect the data query amount of the algorithm in the differential privacy protection model, and the single peak function is used to detect the query function strength of the algorithm in the differential privacy protection model. In the experiment, firstly, measure the big data text clustering algorithm, replace the algorithm into the function, repeatedly query the data 20 times, record each query result, and calculate the mean, variance, and range equivalence of the query result. The big data text clustering algorithm is used to confirm the data convergence effect of multimodal function, find the best peak of function in the algorithm, and find the periodic optimal solution of function in the multi peak environment. Table2 shows the parameter settings of each function in the algorithm.Table 2
Parameter settings.
Configuration itemConfiguration parameterConfiguration itemConfiguration parameterOperation modeSpark2.1.0Operating systemMicrosoft windows ws7×64Hadoop version2.6.0Memory8 GB DDR4 2400 MHzJDK version1.8.0CPUIntel(R) i5-6402PDominant frequency2.80GHzImplementation languageJava 1.8.0_101-b13In order to meet the needs of simulation and comparison experiments for large data sets of real Internet of Things locations, three location data sets are downloaded from the Internet of Things database geolife GPS trajectories, namely, gewara, geolife, and Amazon. The gewara location data set includes the check-in time, location, and transaction records of the user’s social network. Geolife location data set includes roads, geographical locations, and user footprints. The Amazon location data set contains the user’s text location information. The above three location data sets contain all kinds of location information and nonlocation information of users, which meet the needs of simulation and comparison experiments. During the experiment, the algorithm is compiled by visual c + + and tested by MATLAB simulation software. The experimental environment selected is as follows: the central processor is Intel dual core 2.7 GB, the memory is 4 GB, the hard disk capacity is 500 GB, the operating system is window10, and Visual C + + programming is adopted. The basic description of the structure of the experimental data set is shown in Table3.Table 3
Basic description of experimental data set structure.
Data setGewaraGeolifeAmazonData typeMultivariateMultivariateMultivariateNumber of samples1197655281650544872Number of attributes13711Number of clusters201014In the experiment, in order to ensure the synchronization of different functions in the algorithm, it is necessary to set the iterative operation of the same dimension in the particle population. Each time, the function completing the iterative operation will count its own objective function value and the best position of the peak value in the function environment. The Wilkerson two-tailed rank sum test is also used to verify the experimental results. When using the algorithm in this paper to cluster big data with different data sizes, when setting different support thresholds, the cluster differentiation of clustering results is shown in Figure5.Figure 5
Intercluster discrimination results.It can be seen from the experimental results in Figure5 that the algorithm in this paper is used to cluster big data texts, and a higher clustering effect can be obtained. When the support threshold is 0.2, the cluster discrimination of the results of clustering big data texts by the algorithm in this paper is high, which is higher than 4. Therefore, when using the algorithm in this paper to cluster big data texts, the support threshold should be set to 0.2. It shows that each cluster in the big data text clustering result divided by the algorithm in this paper has high discriminative performance, and the clustering effect is better.The algorithm in this paper is used for statistics. When clustering big data with different data volume, when setting different support threshold, the correlation degree between clusters of clustering results is shown in Figure6.Figure 6
Intercluster correlation results.As can be seen from the experimental results in Figure6, when the algorithm in this paper is used to cluster big data, when the support threshold is 0.2, the correlation degree between clusters in the clustering results is high. The reason is to build a differential privacy protection model according to the characteristics of swarm intelligence, digitize the location information, complete the location information preprocessing, complete the differential privacy budget allocation based on KD tree division, and reduce the location information, which is conducive to enhance the correlation between clusters to a certain extent. It is verified that this algorithm can effectively realize the efficient clustering of big data texts. In order to further verify the effectiveness of the big data text clustering algorithm based on swarm intelligence, this algorithm is used to compare the algorithms in reference [7] and reference [8] to verify the clustering effects of the three algorithms. The results are shown in Table 4.Table 4
Clustering effect analysis results of three algorithms.
Performance analysisClustering timeIs it sensitive to abnormal textMemory ratioPaper algorithm1-3 s, short time, and can realize clustering quicklyIt is very sensitive and can quickly obtain abnormal textLow, so it will not occupy a lot of memory due to the large amount of dataReference [7] algorithm10-13 s, relatively long time. When the amount of data is large, the clustering time also increasesIt is sensitive. When an abnormal value appears, it needs to be queried for a certain timeLow, but the memory consumption will increase when the amount of data increasesReference [8] algorithm15-17 s, which takes a long time and cannot efficiently complete a large number of text clusteringNot sensitive, unable to get outliersLarge, which takes up a large amount of memory in clusteringAccording to Table4, among the three clustering algorithms, the clustering time of reference [7] algorithm is relatively long, with a time of 10-13 s, and it is not sensitive to abnormal text. At the same time, the memory occupied in clustering is relatively large, and the clustering time of reference [8] algorithm is too long, with a time of 15-17 s. The comparison shows that the clustering effect of this algorithm is ideal, the clustering time is short, only 1-3 s, is very sensitive to abnormal text, and can quickly obtain abnormal text, and the memory occupation is relatively low. When clustering, it will not occupy a lot of memory due to the increase of data volume and can realize clustering faster and better. The reason is that this algorithm preprocesses the location information. In the big data text, the location information is one of the key information in the user’s personal information. After reducing the dimension of the mixed location information, it is mapped to the plane coordinate axis to optimize the time and realize the digitization of the location information, which is conducive to faster and better clustering to a certain extent.To sum up, the clustering effect of the big data text clustering algorithm based on swarm intelligence is good, the time is short, it can effectively realize the efficient clustering of big data text, and the performance is superior.


## 5. Discussion

The above experimental results show that the big data text clustering algorithm based on swarm intelligence has a good effect, which can provide a more effective guarantee for big data text and help the development of swarm intelligence. In order to get more accurate experimental conclusions, it is necessary to further optimize the proposed algorithm. It can continue to optimize according to the existing clustering analysis to realize the clustering of various forms of big data text clustering. In the practical application of swarm intelligence, there are massive data. When there are many alternatives and decision objectives for big data text clustering, it needs to spend a lot of energy to realize big data text clustering. The massive data contained in the Internet of Things has high complexity. Different events of the Internet of Things big data have high correlation characteristics. The accurate clustering of the Internet of Things big data is of great significance to the efficient development of the application field of the Internet of Things. The big data text clustering algorithm based on swarm intelligence can not only realize the effective clustering of big data in the Internet of Things but also has the advantages of high clustering convergence speed, good clustering effect, and high practical value. It can be applied to the practical application of big data clustering in the Internet of Things. The algorithm test results verify that the algorithm is scientific and can be applied to the big data of the Internet of Things to provide an effective way for big data text clustering.


## 6. Conclusion and Prospect

### 6.1. Conclusion

Swarm intelligence-based big data text clustering algorithm builds a differential privacy protection model according to the characteristics of swarm intelligence, digitizes location information to complete location information preprocessing, completes differential privacy budget allocation based on KD tree division, and reduces dimensionality to cluster locations based on the information. A differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. The fuzzy confidence and support thresholds are used to extract the associated features to determine the text targets of the big data to be clustered, and the target decision matrix is established to obtain the clustering decision results. It has a good effect in the experimental process, and the following conclusions are obtained through the experiment: each cluster in the big data text clustering results divided by the algorithm in this paper has high distinguishing performance, and the clustering effect is better. It can effectively realize the big data text. The clustering effect of the algorithm in this paper is ideal, the clustering time is short, it is very sensitive to abnormal text and can quickly obtain abnormal text, and the memory ratio is low, and it will not be affected by the increase of data volume during clustering. It occupies a lot of memory, can achieve faster and better clustering, and has innovative features.


### 6.2. Prospects

Text clustering is a research hotspot at present. A large number of scholars are actively engaged in the exploration of this field, but there are still great challenges in this field. As for the research content of this paper, the following problems are still worthy of further research:(1)
As the basis of other research fields, such as information retrieval and image processing, text clustering still needs to be improved in terms of accuracy and recall. In the next step, we will continue to explore and study better clustering algorithms in this field(2)
How to better solve the research of swarm intelligence under big data is also the focus of research work(3)
In order to make the text clustering algorithm better deal with big data, the next research can deeply discuss the parallel network decomposition clustering and continue to further study the characteristics of speedup ratio performance, scalability, and scale growth, so as to make the big data text clustering results develop better


## 6.1. Conclusion

Swarm intelligence-based big data text clustering algorithm builds a differential privacy protection model according to the characteristics of swarm intelligence, digitizes location information to complete location information preprocessing, completes differential privacy budget allocation based on KD tree division, and reduces dimensionality to cluster locations based on the information. A differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. The fuzzy confidence and support thresholds are used to extract the associated features to determine the text targets of the big data to be clustered, and the target decision matrix is established to obtain the clustering decision results. It has a good effect in the experimental process, and the following conclusions are obtained through the experiment: each cluster in the big data text clustering results divided by the algorithm in this paper has high distinguishing performance, and the clustering effect is better. It can effectively realize the big data text. The clustering effect of the algorithm in this paper is ideal, the clustering time is short, it is very sensitive to abnormal text and can quickly obtain abnormal text, and the memory ratio is low, and it will not be affected by the increase of data volume during clustering. It occupies a lot of memory, can achieve faster and better clustering, and has innovative features.


## 6.2. Prospects

Text clustering is a research hotspot at present. A large number of scholars are actively engaged in the exploration of this field, but there are still great challenges in this field. As for the research content of this paper, the following problems are still worthy of further research:(1)
As the basis of other research fields, such as information retrieval and image processing, text clustering still needs to be improved in terms of accuracy and recall. In the next step, we will continue to explore and study better clustering algorithms in this field(2)
How to better solve the research of swarm intelligence under big data is also the focus of research work(3)
In order to make the text clustering algorithm better deal with big data, the next research can deeply discuss the parallel network decomposition clustering and continue to further study the characteristics of speedup ratio performance, scalability, and scale growth, so as to make the big data text clustering results develop better



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*Source: 7551035-2022-04-15.xml*