# Promotion Strategy of Ideological and Political Education Management in Colleges and Universities Using Clustering Techniques

Authors: Fei Cao; Zhigang Huang; Yunxuan Weng
Journal: Mobile Information Systems
 (2022)

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## Abstract

Computer-based technologies have changed our daily lives, such as the way we live, interact, learn, and play. Data collected through these technologies are now being used to support the second round of revolution in all of these areas. Data mining (DM) research has progressed rapidly in recent years, and the information design of educational management systems has also progressed significantly. Using big data technologies, several educational management systems have collected a great deal of information. However, the DM implementation in educational management systems is still in its initial stages. The use of DM techniques in educational management systems is theoretically important and practically useful. With the advancement of information technology (IT) in the field of college education, the present ideological and political theory courses in colleges and universities have gradually developed into information-based teaching, which has increased teaching efficiency and quality. This paper investigates the “work assessment quantitative table” of ideological and political education (IAPE) administration in universities using data mining and clustering technology techniques. In addition, this study utilizes the k-means clustering algorithms to analyze the data from the “work assessment quantitative table” of IAPE of university administration, which successfully overcomes the limitations and inadequacies of standard analysis approaches.


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## Body

## 1. Introduction

Data analysis, data mining (DM), and other branch technologies are successively used in the field of big data technology. Each technological branch provides a particular type of data application [1]. Recently, the big data technology has become increasingly integrated with modern education, and it has been utilized to assess student development and improve teaching techniques, with positive outcomes. DM and machine learning (ML) are used to extract useful patterns from large datasets [2]. DM research began in the 1980s and has been successfully implemented in the fields of marketing, finance, business, and education. In recent years, DM research has made significant progress in the development of an informatization management system [3].The political and ideological theory courses of colleges and universities play an important role in education. It plays an important guiding role in the current political views of colleges and university students and also provides the ideological basis for promoting students’ all-round development. Many schools and colleges analyze and implement an education management system based on a particular situation, and each institution has its own system [4]. Educational management systems perform many activities such as organizing data and information, evaluating histograms, making reports, and printing conclusions.The continuous development of higher education increases the number of high school graduates who are enrolling in colleges and has a significant impact on college teaching and administration staff [5]. Some students do not have strong learning skills, which creates lots of new issues for teachers in the classroom [6]. On the other hand, universities are also facing great ideological and political challenges from college students due to which it is difficult to build an excellent educational environment. Students’ interest and motivation in the study can be substantially influenced by the establishment of a bad learning environment, which is typically a challenging issue for administrators of IAPE in universities [7]. Many ethnic groups at universities have various learning methods, and students’ basic knowledge also varies from student to student. As a result, managing ethnic minority students from various locations is usually tough for the university education management department [8, 9].Under normal conditions, the university’s relevant departments have gathered a huge volume of data via regular education management activities. But data processing is now confined to a low level of search and simple analysis, which cannot discover important and instructional information [10]. So, utilizing the traditional techniques is unfair, and the impact of their management activities cannot be evaluated effectively and appropriately. To overcome the limitations of traditional analysis, we used clustering analysis of DM and used the k-means algorithm of DM to analyze data from the “work assessment quantitative table” of the university administration’s ideological and political education. Clustering is a frequent exploratory data analysis method used to gain an understanding of the data structure [11]. K-means clustering is a data clustering technique that may be used in unsupervised ML. Unlabeled data can be sorted into a predetermined number of clusters based on their characteristics (k) [12]. The K-means clustering method calculates centroids to determine the centroid and then repeats the process to get the optimum centroids. The number of clusters produced by the algorithm from data is represented by the letter “K” in K-means. Due to its scalability, the k-means clustering technique may be used to efficiently handle large datasets [13].The remaining paper is structured as follows. Section2 illustrates the related work of this study; Section 3 shows the material used and the techniques followed in the accomplishment of this study; Section 4 demonstrates the experimental outcomes of this study and a detailed discussion on the attained analysis results; and Section 5 presents the overall theme of the proposed study.


## 2. Related Work

Data mining (DM) is a technique for extracting beneficial information from huge volumes of data that is implicit, previously unknown, and possibly helpful. Rules, constraints, concepts, visualization, patterns, and other types of extracted knowledge can be expressed [14]. DM refers to a decision-making procedure that finds patterns in a set of facts or observations. The K-means clustering technique of DM is mainly used in this research work in order to investigate the evolution of IAPE innovation paths in colleges and universities. Jodylf et al. [15] examined the applications of DM in college IAPE in their research study. They evaluated the current state of IAPE using DM techniques and highlighted the key aspects of IAPE reforms, such as theoretical reform, practical reform, and examination reform. In the context of new media, they also examined the evolution of ideological and political education. They analyzed the data of counselors’ “job evaluation scale” using cluster analysis in DM and the k-means technique. Furthermore, they used the “job evolution scale” in college IAPE management as a data source, and clustering is done on the “work check scale” of counselors [16]. Jiang [17] established a network education platform using cluster analysis to examine the ideological position of college students. The network provides a vital forum for college teachers and students to express themselves and exchange their views, providing the groundwork for the relevance and timeliness of ideological education. Hong [5] investigated the current state of IAPE in colleges and universities using a questionnaire survey and proposed a clustering model using a cloud computing platform.


### 2.1. Data Mining (DM)

DM is a nontrivial method for extracting significant, previously unknown, implicit, and possibly useful information or patterns from large datasets. DM research is divided into two parts: applied research and basic theoretical research. A theoretical framework should be capable of representing common DM activities such as clustering, rule discovery, and classification, as well as discussing the probabilistic nature of observed patterns and models, discussing data and inductive generalizations of data, and accepting the presence of various data types (sequences, relational data, Web data, and text) [18]. The applied DM research group (ADMRG) focuses on the creation and application of DM techniques that help people to make better decisions. ADMRG gives a special focus on developing models based on fine-grained and large-scale behavior data, such as payment, location, and website browsing [19]. Nowadays, DM is used in all aspects of life and has many applications. Figure 1 describes the areas where data mining is widely used.Figure 1
DM applications in different fields.


### 2.2. University Management Strategies for Enhancing Ideological and Political Education

#### 2.2.1. Manage Attitude Strategies

Under the backdrop of college ideological teaching work in the new era, a teacher should fully analyze the individual ideological education idea. At the same time, work on quality education is based on college students’ development demands, ideological work, and scientific guidance for students [20]. Only under the guidance of teachers’ ideological and political ideas, college students can carry out various practical activities, strengthen students’ ability to experience, and effectively solve the deficiencies and contradictions existing in the current stage of IAPE [21].


#### 2.2.2. Manage Ability Improvement Strategies

Strengthen teacher training, introduce, and train several high-quality talents with the knowledge of IT and ideological and political education. Political and ideological work-related topics should also take the initiative to alter their views, adapt to the data mining application environment, master appropriate big data technology, and utilize quantitative analysis competently in educational practice [22]. To encourage rational and effective data exchange, a multifunctional and cross-departmental data mining platform for IAPE at universities and colleges should be established.


#### 2.2.3. Manage Method Improvement Strategies

Administrators of college IAPE should make the student realize their role and value in social development, improve their sense of social responsibility, and stimulate their consciousness of making achievements [23].


#### 2.2.4. Manage Effect Improvement Strategies

The objective of the whole mechanism is to use an effective evaluation method for IAPE in both universities and colleges. The evaluation of the IAPE effect after the use of DM technology is completely based on the judgment of objective data, the conclusion is more real, and the objectives provide systematic support for ensuring the overall effect of IAPE [17]. It is vital to building a long-term supervision system to assure the reasonable progress of evaluation in order to evaluate the influence of IAPE objectively and openly in universities and colleges.


## 2.1. Data Mining (DM)

DM is a nontrivial method for extracting significant, previously unknown, implicit, and possibly useful information or patterns from large datasets. DM research is divided into two parts: applied research and basic theoretical research. A theoretical framework should be capable of representing common DM activities such as clustering, rule discovery, and classification, as well as discussing the probabilistic nature of observed patterns and models, discussing data and inductive generalizations of data, and accepting the presence of various data types (sequences, relational data, Web data, and text) [18]. The applied DM research group (ADMRG) focuses on the creation and application of DM techniques that help people to make better decisions. ADMRG gives a special focus on developing models based on fine-grained and large-scale behavior data, such as payment, location, and website browsing [19]. Nowadays, DM is used in all aspects of life and has many applications. Figure 1 describes the areas where data mining is widely used.Figure 1
DM applications in different fields.


## 2.2. University Management Strategies for Enhancing Ideological and Political Education

### 2.2.1. Manage Attitude Strategies

Under the backdrop of college ideological teaching work in the new era, a teacher should fully analyze the individual ideological education idea. At the same time, work on quality education is based on college students’ development demands, ideological work, and scientific guidance for students [20]. Only under the guidance of teachers’ ideological and political ideas, college students can carry out various practical activities, strengthen students’ ability to experience, and effectively solve the deficiencies and contradictions existing in the current stage of IAPE [21].


### 2.2.2. Manage Ability Improvement Strategies

Strengthen teacher training, introduce, and train several high-quality talents with the knowledge of IT and ideological and political education. Political and ideological work-related topics should also take the initiative to alter their views, adapt to the data mining application environment, master appropriate big data technology, and utilize quantitative analysis competently in educational practice [22]. To encourage rational and effective data exchange, a multifunctional and cross-departmental data mining platform for IAPE at universities and colleges should be established.


### 2.2.3. Manage Method Improvement Strategies

Administrators of college IAPE should make the student realize their role and value in social development, improve their sense of social responsibility, and stimulate their consciousness of making achievements [23].


### 2.2.4. Manage Effect Improvement Strategies

The objective of the whole mechanism is to use an effective evaluation method for IAPE in both universities and colleges. The evaluation of the IAPE effect after the use of DM technology is completely based on the judgment of objective data, the conclusion is more real, and the objectives provide systematic support for ensuring the overall effect of IAPE [17]. It is vital to building a long-term supervision system to assure the reasonable progress of evaluation in order to evaluate the influence of IAPE objectively and openly in universities and colleges.


## 2.2.1. Manage Attitude Strategies

Under the backdrop of college ideological teaching work in the new era, a teacher should fully analyze the individual ideological education idea. At the same time, work on quality education is based on college students’ development demands, ideological work, and scientific guidance for students [20]. Only under the guidance of teachers’ ideological and political ideas, college students can carry out various practical activities, strengthen students’ ability to experience, and effectively solve the deficiencies and contradictions existing in the current stage of IAPE [21].


## 2.2.2. Manage Ability Improvement Strategies

Strengthen teacher training, introduce, and train several high-quality talents with the knowledge of IT and ideological and political education. Political and ideological work-related topics should also take the initiative to alter their views, adapt to the data mining application environment, master appropriate big data technology, and utilize quantitative analysis competently in educational practice [22]. To encourage rational and effective data exchange, a multifunctional and cross-departmental data mining platform for IAPE at universities and colleges should be established.


## 2.2.3. Manage Method Improvement Strategies

Administrators of college IAPE should make the student realize their role and value in social development, improve their sense of social responsibility, and stimulate their consciousness of making achievements [23].


## 2.2.4. Manage Effect Improvement Strategies

The objective of the whole mechanism is to use an effective evaluation method for IAPE in both universities and colleges. The evaluation of the IAPE effect after the use of DM technology is completely based on the judgment of objective data, the conclusion is more real, and the objectives provide systematic support for ensuring the overall effect of IAPE [17]. It is vital to building a long-term supervision system to assure the reasonable progress of evaluation in order to evaluate the influence of IAPE objectively and openly in universities and colleges.


## 3. Materials and Methods

The materials and methods of the proposed research work are briefly described in the following sections.


### 3.1. Analysis Process

Data mining (DM) is a method of confirming a strategy and conducting comprehensive data research and evaluation. The use of DM in the inspection system of IAPE administrators’ work evaluations at colleges and universities is particularly significant [24]. Most evidence regarding the accuracy of IAPE management at universities were previously collected by searching a huge number of data [25]. This research proposes the cluster investigation DM technique for dealing with the data and information of IAPE management at universities, which can convert a huge amount of data into cluster findings and improves the use of this kind of data. Figure 2 depicts the data mining procedure.Figure 2
Overall process of DM operations.


#### 3.1.1. Dataset Collection

The development of a dataset that has a strong connection with the target class is one of the most significant and fundamental steps in the development of an intelligent system. Keeping the importance of the dataset into consideration in 2019, we collected and sorted the “work assessment quantitative table” of the university administration’s IAPE (see appendix for sample table). We sorted 210 quantitative assessment tables related to the educational management of IAPE administrators in universities. We used the k-means clustering algorithm of DM to analyze the data from the “work assessment quantitative table” of IAPE of university administration and draw some major conclusions which provide exceptional guiding values for management and instructions.


#### 3.1.2. Data Preprocessing Techniques

Data preprocessing is the process of preparing raw data for use in a model. It is a necessary step for cleaning and formatting data before it can be utilized in a model and is an essential step for cleaning and structuring data before using it in a model, which improves the model’s accuracy and efficiency. Data cleaning and conversion are the two main aspects of data preprocessing.(1) Data Cleaning. When the data are combined/integrated from multiple sources, there are opportunities for data mislabeled or duplication. Data cleaning is the process of removing or correcting corrupted, erroneous, duplicate, incorrectly formatted, or incomplete data from a dataset.(2) Data Conversion. Data conversion is the process of transforming data from one form to another. While data translation may appear to be a simple concept, it is an essential stage in the data integration procedure.


### 3.2. Data Types in Cluster Investigation

Enable clustering complexities to the arrangement withn data items of various kinds. In most cases, the data types in cluster investigation primarily have two categories, which are listed below.


#### 3.2.1. Matrix Data

Interval scaling is used to measure certain data types which are subsequently represented as a related table or ann × p matrix.(1)X11X12…X1PX21X22…X2P⋯⋯⋯⋯XN1XN2…XNP.


#### 3.2.2. Matrix Dissimilarity

The matrix dissimilarity is utilized to determine the degree of similarity between two pairs ofn items, and its formal expression is the m × m matrix.(2)0S2,10S3,1S3,20⋅⋅⋅⋅⋅⋅⋅⋅⋅Sm,1Sm,2..0.For phase difference,S(i, j) is used, where S(i, j) is the quantum mechanical system of the degree of dissimilarity among two items i and j, which is usually larger than or equal to zero. The measurement methods of phase difference di,j include the following.(1) Interval Scaling Variables. The distance between each set of items is commonly used to quantify the difference or resemblance between objects, defined by interval scale variables, which include(3)Si,j=xi1−xj12+xi2−xj22+⋯+xin−xjn2.In the above equation,i and j are n-dimensional data substances. The distance metric is Manhattan, which is another standard distance metric and is described as follows:(4)Si,j=xi1−xj1+xi2−xj2+⋯+xin−xjn.The Minkowski coldness is a generalization form of the two distances mentioned above. The following formula is used to describe it.(5)Si,j=xi−xj=∑k=1nxik−xjkp1/p.(2) Binary Variables. Each variable can be in one of two states: 0 or 1, 0 indicating that it is empty and 1 indicating that it is present. Based on this, a conditional dependency table is obtained that assumes the weights of all binary variables are the same as shown in Table 1.Table 1
Conditions for binary variables.
Binary variables10Total0xyx + y1qrq + rTotalx + qy + rTFurthermore, it describes the degree of difference between itemi and object j which may be calculated using the following formula:(6)Si,j=x+rq+x+r+y.(3) Categorical Variables. Binary variables are expanded into categorical variables. The categorical variable normally has more than one state as compared to the binary variable. Type O, type A, type B, and type AB are four preferable state values for the classification variables. The phase difference between classification variables i and j is calculated using the following formula:(7)Si,j=T−nT.In the above formula,m refers to the number of competitions that is the amount of variable quantity in the same state when “i” and “j” are taken, and “p” refers to the number of all variables.(4) Ordinal Variables. Discrete ordinal variables and continuous ordinal variables are the two types of ordinal variables. Classified variables are compared with discrete ordinal variables.(5) Mixed Type Variables. If there are “p” mixed-type variables in the database, the degree of variation between objects “i” and “j” can be measured by the following formula:(8)di,j=∑f=1pδiffdiff∑f=1pδiff.In (8), the variable f gives the calculation of the difference between i and j, diff is calculated according to its kind, if there is no metric value between objects. When the variable is not a symmetric binary variable, then the indicator term δiff is equal to 0. The variable f gives the computation of the dissimilarity between i and j, and xif=xjf=0 is computed according to its type.


### 3.3. K-Means Clustering Algorithm Implementation

K-means clustering is a data clustering technique that may be used in unsupervised machine learning (ML). Unlabeled data can be sorted into a predetermined number of clusters based on their characteristics (k). The selected similarity measuring method will have an impact on the k-means clustering process. To improve clustering performance, similarity measurement systems utilize the error square criterion function. Assume that X consists of K cluster subsets x1, x2, x3,…,xk; each cluster subset has n1, n2, n3,..., nk samples; and each cluster subset’s mean points are m1, m2, m3,...,mk. The following formula is used to calculate the error square sum criteria function:(9)E=∑i=1n∑p∈Xip−mi2.Mark each sample as the category closest to the categories center, namely,(10)labeli=argmin1≤j≤kXi−mj.Replace the mean of all samples in each category with the center of that category:(11)mj=1nj∑i∈njXi.The following steps are required to implement the K-means clustering algorithm:(i)
To calculate similarity, the average value of items in a cluster is utilized(ii)
Designatek cluster center at random(iii)
Using the rule of minimal distance, assign each sampleXi to the class with the closest neighbor(iv)
Move the cluster’s sample mean to the cluster’s center(v)
Keep repeating steps 2 and 3 until the cluster’s center remains unchangedIn this research, we employed clustering analysis in DM and k-means algorithms to evaluate data from the “work assessment quantitative table” of university administration’s ideological and political education.


## 3.1. Analysis Process

Data mining (DM) is a method of confirming a strategy and conducting comprehensive data research and evaluation. The use of DM in the inspection system of IAPE administrators’ work evaluations at colleges and universities is particularly significant [24]. Most evidence regarding the accuracy of IAPE management at universities were previously collected by searching a huge number of data [25]. This research proposes the cluster investigation DM technique for dealing with the data and information of IAPE management at universities, which can convert a huge amount of data into cluster findings and improves the use of this kind of data. Figure 2 depicts the data mining procedure.Figure 2
Overall process of DM operations.


### 3.1.1. Dataset Collection

The development of a dataset that has a strong connection with the target class is one of the most significant and fundamental steps in the development of an intelligent system. Keeping the importance of the dataset into consideration in 2019, we collected and sorted the “work assessment quantitative table” of the university administration’s IAPE (see appendix for sample table). We sorted 210 quantitative assessment tables related to the educational management of IAPE administrators in universities. We used the k-means clustering algorithm of DM to analyze the data from the “work assessment quantitative table” of IAPE of university administration and draw some major conclusions which provide exceptional guiding values for management and instructions.


### 3.1.2. Data Preprocessing Techniques

Data preprocessing is the process of preparing raw data for use in a model. It is a necessary step for cleaning and formatting data before it can be utilized in a model and is an essential step for cleaning and structuring data before using it in a model, which improves the model’s accuracy and efficiency. Data cleaning and conversion are the two main aspects of data preprocessing.(1) Data Cleaning. When the data are combined/integrated from multiple sources, there are opportunities for data mislabeled or duplication. Data cleaning is the process of removing or correcting corrupted, erroneous, duplicate, incorrectly formatted, or incomplete data from a dataset.(2) Data Conversion. Data conversion is the process of transforming data from one form to another. While data translation may appear to be a simple concept, it is an essential stage in the data integration procedure.


## 3.1.1. Dataset Collection

The development of a dataset that has a strong connection with the target class is one of the most significant and fundamental steps in the development of an intelligent system. Keeping the importance of the dataset into consideration in 2019, we collected and sorted the “work assessment quantitative table” of the university administration’s IAPE (see appendix for sample table). We sorted 210 quantitative assessment tables related to the educational management of IAPE administrators in universities. We used the k-means clustering algorithm of DM to analyze the data from the “work assessment quantitative table” of IAPE of university administration and draw some major conclusions which provide exceptional guiding values for management and instructions.


## 3.1.2. Data Preprocessing Techniques

Data preprocessing is the process of preparing raw data for use in a model. It is a necessary step for cleaning and formatting data before it can be utilized in a model and is an essential step for cleaning and structuring data before using it in a model, which improves the model’s accuracy and efficiency. Data cleaning and conversion are the two main aspects of data preprocessing.(1) Data Cleaning. When the data are combined/integrated from multiple sources, there are opportunities for data mislabeled or duplication. Data cleaning is the process of removing or correcting corrupted, erroneous, duplicate, incorrectly formatted, or incomplete data from a dataset.(2) Data Conversion. Data conversion is the process of transforming data from one form to another. While data translation may appear to be a simple concept, it is an essential stage in the data integration procedure.


## 3.2. Data Types in Cluster Investigation

Enable clustering complexities to the arrangement withn data items of various kinds. In most cases, the data types in cluster investigation primarily have two categories, which are listed below.


### 3.2.1. Matrix Data

Interval scaling is used to measure certain data types which are subsequently represented as a related table or ann × p matrix.(1)X11X12…X1PX21X22…X2P⋯⋯⋯⋯XN1XN2…XNP.


### 3.2.2. Matrix Dissimilarity

The matrix dissimilarity is utilized to determine the degree of similarity between two pairs ofn items, and its formal expression is the m × m matrix.(2)0S2,10S3,1S3,20⋅⋅⋅⋅⋅⋅⋅⋅⋅Sm,1Sm,2..0.For phase difference,S(i, j) is used, where S(i, j) is the quantum mechanical system of the degree of dissimilarity among two items i and j, which is usually larger than or equal to zero. The measurement methods of phase difference di,j include the following.(1) Interval Scaling Variables. The distance between each set of items is commonly used to quantify the difference or resemblance between objects, defined by interval scale variables, which include(3)Si,j=xi1−xj12+xi2−xj22+⋯+xin−xjn2.In the above equation,i and j are n-dimensional data substances. The distance metric is Manhattan, which is another standard distance metric and is described as follows:(4)Si,j=xi1−xj1+xi2−xj2+⋯+xin−xjn.The Minkowski coldness is a generalization form of the two distances mentioned above. The following formula is used to describe it.(5)Si,j=xi−xj=∑k=1nxik−xjkp1/p.(2) Binary Variables. Each variable can be in one of two states: 0 or 1, 0 indicating that it is empty and 1 indicating that it is present. Based on this, a conditional dependency table is obtained that assumes the weights of all binary variables are the same as shown in Table 1.Table 1
Conditions for binary variables.
Binary variables10Total0xyx + y1qrq + rTotalx + qy + rTFurthermore, it describes the degree of difference between itemi and object j which may be calculated using the following formula:(6)Si,j=x+rq+x+r+y.(3) Categorical Variables. Binary variables are expanded into categorical variables. The categorical variable normally has more than one state as compared to the binary variable. Type O, type A, type B, and type AB are four preferable state values for the classification variables. The phase difference between classification variables i and j is calculated using the following formula:(7)Si,j=T−nT.In the above formula,m refers to the number of competitions that is the amount of variable quantity in the same state when “i” and “j” are taken, and “p” refers to the number of all variables.(4) Ordinal Variables. Discrete ordinal variables and continuous ordinal variables are the two types of ordinal variables. Classified variables are compared with discrete ordinal variables.(5) Mixed Type Variables. If there are “p” mixed-type variables in the database, the degree of variation between objects “i” and “j” can be measured by the following formula:(8)di,j=∑f=1pδiffdiff∑f=1pδiff.In (8), the variable f gives the calculation of the difference between i and j, diff is calculated according to its kind, if there is no metric value between objects. When the variable is not a symmetric binary variable, then the indicator term δiff is equal to 0. The variable f gives the computation of the dissimilarity between i and j, and xif=xjf=0 is computed according to its type.


## 3.2.1. Matrix Data

Interval scaling is used to measure certain data types which are subsequently represented as a related table or ann × p matrix.(1)X11X12…X1PX21X22…X2P⋯⋯⋯⋯XN1XN2…XNP.


## 3.2.2. Matrix Dissimilarity

The matrix dissimilarity is utilized to determine the degree of similarity between two pairs ofn items, and its formal expression is the m × m matrix.(2)0S2,10S3,1S3,20⋅⋅⋅⋅⋅⋅⋅⋅⋅Sm,1Sm,2..0.For phase difference,S(i, j) is used, where S(i, j) is the quantum mechanical system of the degree of dissimilarity among two items i and j, which is usually larger than or equal to zero. The measurement methods of phase difference di,j include the following.(1) Interval Scaling Variables. The distance between each set of items is commonly used to quantify the difference or resemblance between objects, defined by interval scale variables, which include(3)Si,j=xi1−xj12+xi2−xj22+⋯+xin−xjn2.In the above equation,i and j are n-dimensional data substances. The distance metric is Manhattan, which is another standard distance metric and is described as follows:(4)Si,j=xi1−xj1+xi2−xj2+⋯+xin−xjn.The Minkowski coldness is a generalization form of the two distances mentioned above. The following formula is used to describe it.(5)Si,j=xi−xj=∑k=1nxik−xjkp1/p.(2) Binary Variables. Each variable can be in one of two states: 0 or 1, 0 indicating that it is empty and 1 indicating that it is present. Based on this, a conditional dependency table is obtained that assumes the weights of all binary variables are the same as shown in Table 1.Table 1
Conditions for binary variables.
Binary variables10Total0xyx + y1qrq + rTotalx + qy + rTFurthermore, it describes the degree of difference between itemi and object j which may be calculated using the following formula:(6)Si,j=x+rq+x+r+y.(3) Categorical Variables. Binary variables are expanded into categorical variables. The categorical variable normally has more than one state as compared to the binary variable. Type O, type A, type B, and type AB are four preferable state values for the classification variables. The phase difference between classification variables i and j is calculated using the following formula:(7)Si,j=T−nT.In the above formula,m refers to the number of competitions that is the amount of variable quantity in the same state when “i” and “j” are taken, and “p” refers to the number of all variables.(4) Ordinal Variables. Discrete ordinal variables and continuous ordinal variables are the two types of ordinal variables. Classified variables are compared with discrete ordinal variables.(5) Mixed Type Variables. If there are “p” mixed-type variables in the database, the degree of variation between objects “i” and “j” can be measured by the following formula:(8)di,j=∑f=1pδiffdiff∑f=1pδiff.In (8), the variable f gives the calculation of the difference between i and j, diff is calculated according to its kind, if there is no metric value between objects. When the variable is not a symmetric binary variable, then the indicator term δiff is equal to 0. The variable f gives the computation of the dissimilarity between i and j, and xif=xjf=0 is computed according to its type.


## 3.3. K-Means Clustering Algorithm Implementation

K-means clustering is a data clustering technique that may be used in unsupervised machine learning (ML). Unlabeled data can be sorted into a predetermined number of clusters based on their characteristics (k). The selected similarity measuring method will have an impact on the k-means clustering process. To improve clustering performance, similarity measurement systems utilize the error square criterion function. Assume that X consists of K cluster subsets x1, x2, x3,…,xk; each cluster subset has n1, n2, n3,..., nk samples; and each cluster subset’s mean points are m1, m2, m3,...,mk. The following formula is used to calculate the error square sum criteria function:(9)E=∑i=1n∑p∈Xip−mi2.Mark each sample as the category closest to the categories center, namely,(10)labeli=argmin1≤j≤kXi−mj.Replace the mean of all samples in each category with the center of that category:(11)mj=1nj∑i∈njXi.The following steps are required to implement the K-means clustering algorithm:(i)
To calculate similarity, the average value of items in a cluster is utilized(ii)
Designatek cluster center at random(iii)
Using the rule of minimal distance, assign each sampleXi to the class with the closest neighbor(iv)
Move the cluster’s sample mean to the cluster’s center(v)
Keep repeating steps 2 and 3 until the cluster’s center remains unchangedIn this research, we employed clustering analysis in DM and k-means algorithms to evaluate data from the “work assessment quantitative table” of university administration’s ideological and political education.


## 4. Results and Discussion

### 4.1. Algorithm Path

First of all, the first three samples were selected as clustering centers, and then, the Euclidean distance was used to divide the data points into the nearest cluster. Finally, the method was separated into the mean vector of points in each cluster, which served as the new center point for the iterative process. The specific algorithm consists of a datasetD = {x_1,..,x_n}, and its purpose is to find k clusters {c_1,..,c_k}. Algorithm 1 is described as follows:Algorithm 1: Pseudocode of the algorithm.
As in clustering centers, the firstk samples were used.While changes do in clusteringckForming clustering;Fork = 1,…,n dock  = {x belong to DID Rk,x≤DRj,xj=1,…,k,j≠k}End


### 4.2. Data Clustering Mining

There are 15 items of assessment grade table of work in the quantification, as well as the after-class mastering situation, comments, and suggestions. In order to synthesize these data items into a cluster analysis model, this study reorganizes and merges the data in the quantitative table of job appraisal in four qualities such as “management attitude,” “management ability,” “management approach,” and “management effect.” The five grades of the assessment rating such as “excellent, good, qualified, poor, very poor” are arranged in order with special meaning. According to the formula, the five values of assessment rating can be calculated as 1, 0.75, 0.5, 0.25, and 0. The values of the four attributes are measured by means of the arithmetic average of the items they contain and are illustrated as follows.Management attitude = (adhere to the standard + get along well with classmates + talk properly + business objective)/4Management ability = (identify the condition of difficult students + accurately grasp the situation of special student groups + investigate and punish students who violate rules strictly + competent + conscientiously organize students to do a good job in evaluation)/5Management method = (go to classes and dormitories more than 3 times a week + check dormitory hygiene once a week + talk with students in preparation during the school year + actively participate in and check students’ morning running + distribute student awards and grants in place)/5Management effect = (actively participate in or host group class meetings + understand the situation after class + comments and suggestions)/3As a consequence of the preprocessing information of data samples, the datasets used in the analytical techniques are provided in Table2.Table 2
Analytical procedure of data samples used in the management.
AttitudeAbilityMethodEffect0.520.610.550.570.560.690.550.570.310.350.300.310.770.770.670.650.820.820.810.84The reference value for the K-means algorithms is the typical value of the items in the cluster. While the K-center method uses the point object at the center of the cluster as the reference value to calculate the dissimilarity. Figure3 illustrates the values of the four attributes.Figure 3
Analytical procedure of data samples used in the management.


### 4.3. Sample Improvement

In this process, more measures were taken to improve the K-means algorithm, and at the beginning, the samples representing the three levels were replaced with the first three defined sample data as the clustering center, so, as to reduce the skewness of the data and the times of execution as much as possible. The sample data representing the three levels are shown in Table3 and Figure 4.Table 3
Sample data representing the three levels.
LevelsAttitudeAbilityMethodEffectWorst0.250.250.250.25Average0.500.500.500.50Best0.750.750.750.75Figure 4
Data sample from the three levels.Using the Notepad editor, the km.dat form database file is used to prepare forlater use. In this database file, set (S) is represented as a sample which is described as follows.Npat = 114 + 6 = 120Size Vect = 4Nclust = 30.75, 0.75, 0.75, 0.750.50, 0.50, 0.50, 0.500.25, 0.25, 0.25, 0.25Data input format is shown in Table4.Table 4
Data input format.
Npat……IntSize Vect……IntNclust……IntVect[1, 1]…Vect[1, Size Vect]RealVect[2, 1]…Vect[2, Size Vect]RealVect[Npat, 1]…Vect[Nclust, Size Vect]Real


### 4.4. Clustering Results

The results of the final excavations are shown in Table5 and Figure 5.Table 5
The completed excavation’s outcome.
ClusterAttitudeAbilityMethodEffectC-10.780.790.750.76C-20.630.520.540.53C-30.300.300.270.29Figure 5
Results of the final excavations.We acquired the full quantitative scores of 2332 students from the School of Computer Science in 2019. To validate the ultimate findings of DM, the data were collected from 10 activities conducted by the school. To rate these data samples, Table6 and Figure 6 show the final division results after counting the overall score as 100 points and classifying it on a scale of 0 to 100.Table 6
The final division results.
ScoresNumber of samplesPercentage>8050921.8360–80158367.88<6024010.29Figure 6
Results of final division.From the obtained experimental results, it has been demonstrated that the DM model based on the quantitative table of IAPE managers’ job analysis is an actual positive model. It brings positive improvements in IAPE management and educational efforts in universities and colleges.


## 4.1. Algorithm Path

First of all, the first three samples were selected as clustering centers, and then, the Euclidean distance was used to divide the data points into the nearest cluster. Finally, the method was separated into the mean vector of points in each cluster, which served as the new center point for the iterative process. The specific algorithm consists of a datasetD = {x_1,..,x_n}, and its purpose is to find k clusters {c_1,..,c_k}. Algorithm 1 is described as follows:Algorithm 1: Pseudocode of the algorithm.
As in clustering centers, the firstk samples were used.While changes do in clusteringckForming clustering;Fork = 1,…,n dock  = {x belong to DID Rk,x≤DRj,xj=1,…,k,j≠k}End


## 4.2. Data Clustering Mining

There are 15 items of assessment grade table of work in the quantification, as well as the after-class mastering situation, comments, and suggestions. In order to synthesize these data items into a cluster analysis model, this study reorganizes and merges the data in the quantitative table of job appraisal in four qualities such as “management attitude,” “management ability,” “management approach,” and “management effect.” The five grades of the assessment rating such as “excellent, good, qualified, poor, very poor” are arranged in order with special meaning. According to the formula, the five values of assessment rating can be calculated as 1, 0.75, 0.5, 0.25, and 0. The values of the four attributes are measured by means of the arithmetic average of the items they contain and are illustrated as follows.Management attitude = (adhere to the standard + get along well with classmates + talk properly + business objective)/4Management ability = (identify the condition of difficult students + accurately grasp the situation of special student groups + investigate and punish students who violate rules strictly + competent + conscientiously organize students to do a good job in evaluation)/5Management method = (go to classes and dormitories more than 3 times a week + check dormitory hygiene once a week + talk with students in preparation during the school year + actively participate in and check students’ morning running + distribute student awards and grants in place)/5Management effect = (actively participate in or host group class meetings + understand the situation after class + comments and suggestions)/3As a consequence of the preprocessing information of data samples, the datasets used in the analytical techniques are provided in Table2.Table 2
Analytical procedure of data samples used in the management.
AttitudeAbilityMethodEffect0.520.610.550.570.560.690.550.570.310.350.300.310.770.770.670.650.820.820.810.84The reference value for the K-means algorithms is the typical value of the items in the cluster. While the K-center method uses the point object at the center of the cluster as the reference value to calculate the dissimilarity. Figure3 illustrates the values of the four attributes.Figure 3
Analytical procedure of data samples used in the management.


## 4.3. Sample Improvement

In this process, more measures were taken to improve the K-means algorithm, and at the beginning, the samples representing the three levels were replaced with the first three defined sample data as the clustering center, so, as to reduce the skewness of the data and the times of execution as much as possible. The sample data representing the three levels are shown in Table3 and Figure 4.Table 3
Sample data representing the three levels.
LevelsAttitudeAbilityMethodEffectWorst0.250.250.250.25Average0.500.500.500.50Best0.750.750.750.75Figure 4
Data sample from the three levels.Using the Notepad editor, the km.dat form database file is used to prepare forlater use. In this database file, set (S) is represented as a sample which is described as follows.Npat = 114 + 6 = 120Size Vect = 4Nclust = 30.75, 0.75, 0.75, 0.750.50, 0.50, 0.50, 0.500.25, 0.25, 0.25, 0.25Data input format is shown in Table4.Table 4
Data input format.
Npat……IntSize Vect……IntNclust……IntVect[1, 1]…Vect[1, Size Vect]RealVect[2, 1]…Vect[2, Size Vect]RealVect[Npat, 1]…Vect[Nclust, Size Vect]Real


## 4.4. Clustering Results

The results of the final excavations are shown in Table5 and Figure 5.Table 5
The completed excavation’s outcome.
ClusterAttitudeAbilityMethodEffectC-10.780.790.750.76C-20.630.520.540.53C-30.300.300.270.29Figure 5
Results of the final excavations.We acquired the full quantitative scores of 2332 students from the School of Computer Science in 2019. To validate the ultimate findings of DM, the data were collected from 10 activities conducted by the school. To rate these data samples, Table6 and Figure 6 show the final division results after counting the overall score as 100 points and classifying it on a scale of 0 to 100.Table 6
The final division results.
ScoresNumber of samplesPercentage>8050921.8360–80158367.88<6024010.29Figure 6
Results of final division.From the obtained experimental results, it has been demonstrated that the DM model based on the quantitative table of IAPE managers’ job analysis is an actual positive model. It brings positive improvements in IAPE management and educational efforts in universities and colleges.


## 5. Conclusion

In recent years, researchers and experts in the fields of AI and database management systems have started to take more interest in DM technologies. Clustering analysis is one of the core concepts of DM that involves the study of grouping actual or intellectual data elements into a group. In this paper, the basic concept, applications, and development prospects of DM and cluster analysis are discussed. Then, the data types, analysis process, and main clustering methods of cluster analysis are analyzed. In this article, we used the k-means clustering algorithm of DM to analyze the data from the “work assessment quantitative table” of IAPE of university administration and draw some major conclusions which provide exceptional guiding values for management and instructions. The use of cluster analysis in IAPE management in universities is investigated and implemented based on a number of parameters. There are 15 items of assessment grade table of work in the quantification, as well as the after-class mastering situation, comments, and suggestions. This study recombines data from four areas of work evaluation in a quantitative table, which include “management attitude,” “management ability,” “management approach,” and “management effect” which calculates the arithmetic mean value of the data. From the experimental results, it is obvious that the analysis method of the proposed system is way better than the earlier approaches. Furthermore, this method will be more helpful for the college and university administrations to implement in their corresponding institutions and will help the students in getting high grades and understanding the ideological and political education.



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*Source: 3179260-2022-06-17.xml*