# Association Rule Mining Algorithm in College Students’ Quality Evaluation System

Authors: Jing Lei
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

An association rule mining algorithm is an algorithm that mines the association between things and is often used to mine the association knowledge between things. Association rule mining algorithms can find potential connections between different qualities of college students from the data of college students’ life and learning, which can help teachers discover the problems and their own strengths of different students and achieve teaching according to their aptitude. The purpose of this paper is to solve some problems related to the associative rule extraction algorithm and to investigate the impact of applying the associative rule extraction algorithm in a college student quality assessment system. Based on the algorithm, a quality assessment system for college students has been developed. A modified script-based associative rule extraction algorithm is used to find the correlation between the quality and the ability of college students. The quality assessment data of college students are analyzed and studied. The results show that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33% and reduce the probability of outliers in the scoring process by 27%. It can be seen that the association rule extraction algorithm can be applied to college students’ quality assessment system and also reduces the probability of encountering obstacles in accuracy and performance assessment. At the same time, this experiment also proves the robustness and feasibility of the algorithm in this paper.


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## Body

## 1. Introduction

The continued advancement of higher education in China is also increasing the number of college students employed. A large number of difficulties in managing the personal information of college students have increased the complexity of managing information about college students, forcing college student management-related departments and student supervisors. It has become increasingly difficult to accurately determine the survival and development status of college students and increasingly difficult to predict the future development trends of college students. Moreover, with the continuous deepening of vocational education and training reform of the quality of education in colleges and universities, the implementation of higher quality education has gradually become a general consensus in the country and society. The long-term and challenging task of higher education development is to continue to introduce and improve quality education. For this reason, colleges and universities urgently need an information management system with decision support functions. The associative rule retrieval algorithm is a feasible and effective method for solving this problem. Therefore, the association rule mining algorithm plays an important role in this research.Accurate, reliable, and trustworthy learning rules associated with education, which can be extracted from a lot of current information about education and learning, are important for the development of the education information system and have an important managerial application to promote education and learning reform [1]. Schools can use the association rules to reveal the correlation between students in their studies [2]. Appropriately combine subject courses to make related subjects promote each other and jointly improve the student training model discovered through association rules, reasonably design the course sequence in line with the law of students’ intellectual development, and use subject associations and knowledge associations to improve students’ interest in learning. Schools organize interdisciplinary activities in activity classes, expand the relevance and degree of interdisciplinary students in learning, guide students to start with strong disciplines, improve relatively weak disciplines, and ultimately enable students to develop academically in a balanced manner [3].Luna et al. presented the calculation rules of the associative rule mining algorithm in detail, analyzed the research methods and technique of the most widely used associative rule mining algorithm, and showed the importance and significance of the associative rule mining algorithm in the current evaluation system [4]. Zhang et al. detailed the meaning and concept of association rules and analyzed some problems of association rule mining algorithms and proposed methods to solve such problems that have some leading importance [5]. Li also improved the association rule mining algorithm, created the original apriori algorithm, conducted a series of analyses on the weaknesses of the apriori algorithm, added a dihedral angle criterion to the association rule, and proposed solutions to improve the apriori algorithm and problems [6]. Devaraju and Ramakrishnan in a paper presented the classical FP-growth algorithm, discussed and analyzed the advantages and disadvantages of this widely used association rule algorithm, and then presented an improved FP-growth algorithm for FP binary sorting tree algorithm; then this paper proposed an improved algorithm for FP-FBS algorithm based on CAN-tree idea proposed a complete concept of the binary sorting tree [7]. Rahman and Dash proposed a quality assessment model for college students based on an associative rule extraction algorithm, emphasized the importance of a quality assessment system for the comprehensive development of college students, analyzed its characteristics and identified its application area in education, and pointed out the advantages and disadvantages of this model in specific applications [8].In this paper, the research experience and achievements of many predecessors have been summarized and analyzed to study the effect of the associative rule extraction algorithm in college student quality assessment systems. In addition, some innovative applications have been made in this paper. The specific innovations have the following two points: first, the indicators and weights in the quality evaluation model were determined according to the subjective analysis method, which focused on the weighting concept and method steps of the analytic hierarchy process based on the G1 method and applied the method to the quality evaluation system indicators. Second, after obtaining the original data of college students’ quality assessment, the advanced computer programming knowledge and FP-Growth algorithm are used to analyze and process various data of college students’ comprehensive quality and are combined with the original college students’ quality assessment system to form a set. Data scientific research produces evaluation results and evaluation systems to obtain relevant rules as needed. In addition, this paper proves the effectiveness of the algorithm by testing the algorithm proposed in this paper.


## 2. Data-Mining-Related Algorithms

### 2.1. Data Mining Technology

Data mining refers to the process of searching for information hidden in a large amount of data through algorithms. Data mining aims at extracting valuable information from seemingly disparate data sets in order to improve users’ ability to understand the data. We not only can understand the process of “data mining” as an important step in the process of combing through knowledge points but also can consider the whole process of discovering knowledge points as a process based solely on data analysis and extraction [9]. Since an in-depth study of data mining includes not only an in-depth study of data mining analysis algorithms but also how to obtain data quickly for better analysis, an in-depth analysis of preprocessing analysis techniques is also very necessary [10]. To summarize, we believe that the process of human data analysis and mining is a global process of innovative human knowledge discovery [11]. Therefore, the processing of big data analysis and mining mainly includes big data analysis preprocessing, data mining, statistical analysis of results, and data presentation. Specific details of the data mining process are shown in Figure 1.Figure 1
Specific details of the process of data mining.The data extraction process involves the following steps.


#### 2.1.1. Data Cleaning

There is a temporal gap between the specific data that are provided in the real data world and the specific data that are provided in the traditional analysis and data mining [12, 13]. The purpose of data gap cleaning is mainly to reduce the direct impact of these gaps on the final results of a data mining experiment [14]. The main operational techniques for cleaning up data gaps are usually: timely correction of some missing noise values, processing of smooth noise data, and temporal correction of inconsistent noise data [15].


#### 2.1.2. Data Integration

When the massive data you receive from a data center mostly come from multiple types of database repositories, you often need a source of data warehouse integrators to help us quickly complete the effective integration of this data [16]. Data analysis integration technology helps effectively reduce duplication and inconsistency of information in the later results analysis data set, which greatly improves the efficiency of later results data analysis and retrieval [17, 18].


#### 2.1.3. Data Reduction

When data for mining come from data sources stored in different structures and the volume of data is very large, data mining technology faces enormous challenges. This is where data reduction techniques can help [19]. Given the integrity of the source data, data reduction techniques can be generalized based on the common characteristics of the data set. This extraction technique helps complete data set reduction to reduce the data size and ensure the accuracy of extraction results.


#### 2.1.4. Data Transformation

Different data mining algorithms are suitable for processing different types of data, so certain transformations are required for data that does not meet the mining requirements [20]. The main operation strategies of data attribute transformation are smoothing (i.e., data noise can be removed), attribute data construction, normalization, and discrete normalization (or, e.g., using the original age-specific base value and labels in the range 0–5, for a total of 8–15 alternatives). The transformation mode of the database can directly make the comprehensive mining process of massive data more effective, and the working mode of mining is easier to understand [21].


#### 2.1.5. Data Mining

In the main step of data mining, you must choose according to the structural characteristics of the data itself and the data functions that the user expects to achieve. The corresponding pattern algorithm is used to automatically extract some implicit data patterns from these data. The optional pattern algorithm types include various types of related data rules, classification, clustering, regression data analysis, decision trees, formula rediscovery, neural networks, and web data mining, each of which focuses on comprehensive analysis and analysis of various data structures from different perspectives data mining [22].


#### 2.1.6. Data Presentation

Each link of the data mining process is closely related, and grasping the accuracy of each link has a non-negligible impact on the mining results [23]. If the front part of the data mining process does not achieve the expected results, it should be tested multiple times in this part until the accuracy is verified before it can enter the data processing of the next part [24]. The mined knowledge can be used to solve current problems or applied to new data sets in order to achieve more intelligent data analysis functions [25]. Therefore, a data mining process is usually iterative.


### 2.2. Common Mining Algorithms for Association Rules

The main purpose of rule information mining in affiliated enterprises is to comprehensively evaluate them by using the two main interest degree evaluation indexes of support and confidence, so how to accurately construct two interest degree evaluations of association rules. Indicators are also an important research content of current research on association rule information mining technology, that is, in our real life. One of the main research objectives of rule information mining in affiliated enterprises is how to find exactly which kind of rule information or which combination of information is the most interesting and useful and how to provide enterprise managers with important data and information.


#### 2.2.1. Clustering andK-Means Algorithm

Cluster analysis is a process of dividing a data clustering object hierarchy into multiple classes or clusters so that the heights of data objects in the same cluster type have a high degree of similarity, and the heights of data objects in different cluster types are also different. They are a kind of comprehensive learning without process supervision. The cluster analysis algorithm can be roughly subdivided into cluster analysis algorithms based on the hierarchical division clustering method, hierarchical classification method, and density comparison method, among whichk-means clustering is a clustering method based on hierarchical partitioning. It selects p or p data object centers from p or n data objects as the initial data clustering object center and assigns it to the Euclidean time-distance calculation formula. The nearest initial clustering object center calculates the average density of each data clustering object in each cluster using a calculation formula and repeatedly superimposes the hierarchical calculation, comparison, and hierarchical classification until the center after the initial clustering no longer changes. The p and m in are the starting point of n data objects, and m is the average density of each data object in each cluster c.(1)Px=fn+1ωμκn+1!x−x0n+1,C=∑k=0nxkk!+mnX.


#### 2.2.2. Apriori Algorithm

Apriori is an algorithm that is one of the most influential alternative mining algorithms for layer-by-layer association data rules. This mining algorithm is based on a superimposed alternative structure idea, also known as a layer-by-layer data search. Denoting the item sets asl and l1, then l1 is used to generate l1 and l2, respectively, and then l2 to generate l2 and l3; repeat this iterative process until you cannot find more frequent item set lists. It needs one scan in the database every time it searches layer by layer. In each data scan, it only needs to consider a frequent k item sets with the same time width function k. The traditional apriori algorithm uses a support-confidence framework to evaluate the value of association rules, but in practical applications, relying only on the limits of support and confidence thresholds will still result in some useless or even wrong association rules. Apriori algorithm often uses the calculation formula shown in the following equations:(2)L=∫x2nx+xn+1n+1+C,(3)FV=VANd1−e−rtDNd2.


#### 2.2.3. FP-Growth Algorithm

The FP-growth algorithm compresses, filters, and mines databases and stores them to form frequent pattern trees. The nodes of the frequent pattern tree have four fields. The first field stores the name of the project. The second field is the number of transactions sharing this prefix. The third field is a link to the name of the next public project in another path. This pointer is called the “link pointer.” The link pointer links all nodes with the same project name in different paths. The fourth field is a list of pointers. Each pointer points to a child node that shares the same prefix as the current node. The item title table contains all items sorted by frequency of occurrence. Each item has a pointer to the first node in the tree with the same item name. You can access this pointer and other link pointers in the tree together. All nodes with the same name are now available. To build frequent pattern trees, you need to read the database twice. Initially, the frequent pattern tree has only one node named “empty” that is, the root node, which does not represent an item in the database. During the second read of the database, each item is converted to a node in the frequent pattern tree, and each transaction is converted to a path in the frequent pattern tree.(4)Fa=a−1stebucstiona∗t+a+1bxvsstna∗t,Sp=∑j=1vMfD∗log2MfD+JV.


## 2.1. Data Mining Technology

Data mining refers to the process of searching for information hidden in a large amount of data through algorithms. Data mining aims at extracting valuable information from seemingly disparate data sets in order to improve users’ ability to understand the data. We not only can understand the process of “data mining” as an important step in the process of combing through knowledge points but also can consider the whole process of discovering knowledge points as a process based solely on data analysis and extraction [9]. Since an in-depth study of data mining includes not only an in-depth study of data mining analysis algorithms but also how to obtain data quickly for better analysis, an in-depth analysis of preprocessing analysis techniques is also very necessary [10]. To summarize, we believe that the process of human data analysis and mining is a global process of innovative human knowledge discovery [11]. Therefore, the processing of big data analysis and mining mainly includes big data analysis preprocessing, data mining, statistical analysis of results, and data presentation. Specific details of the data mining process are shown in Figure 1.Figure 1
Specific details of the process of data mining.The data extraction process involves the following steps.


### 2.1.1. Data Cleaning

There is a temporal gap between the specific data that are provided in the real data world and the specific data that are provided in the traditional analysis and data mining [12, 13]. The purpose of data gap cleaning is mainly to reduce the direct impact of these gaps on the final results of a data mining experiment [14]. The main operational techniques for cleaning up data gaps are usually: timely correction of some missing noise values, processing of smooth noise data, and temporal correction of inconsistent noise data [15].


### 2.1.2. Data Integration

When the massive data you receive from a data center mostly come from multiple types of database repositories, you often need a source of data warehouse integrators to help us quickly complete the effective integration of this data [16]. Data analysis integration technology helps effectively reduce duplication and inconsistency of information in the later results analysis data set, which greatly improves the efficiency of later results data analysis and retrieval [17, 18].


### 2.1.3. Data Reduction

When data for mining come from data sources stored in different structures and the volume of data is very large, data mining technology faces enormous challenges. This is where data reduction techniques can help [19]. Given the integrity of the source data, data reduction techniques can be generalized based on the common characteristics of the data set. This extraction technique helps complete data set reduction to reduce the data size and ensure the accuracy of extraction results.


### 2.1.4. Data Transformation

Different data mining algorithms are suitable for processing different types of data, so certain transformations are required for data that does not meet the mining requirements [20]. The main operation strategies of data attribute transformation are smoothing (i.e., data noise can be removed), attribute data construction, normalization, and discrete normalization (or, e.g., using the original age-specific base value and labels in the range 0–5, for a total of 8–15 alternatives). The transformation mode of the database can directly make the comprehensive mining process of massive data more effective, and the working mode of mining is easier to understand [21].


### 2.1.5. Data Mining

In the main step of data mining, you must choose according to the structural characteristics of the data itself and the data functions that the user expects to achieve. The corresponding pattern algorithm is used to automatically extract some implicit data patterns from these data. The optional pattern algorithm types include various types of related data rules, classification, clustering, regression data analysis, decision trees, formula rediscovery, neural networks, and web data mining, each of which focuses on comprehensive analysis and analysis of various data structures from different perspectives data mining [22].


### 2.1.6. Data Presentation

Each link of the data mining process is closely related, and grasping the accuracy of each link has a non-negligible impact on the mining results [23]. If the front part of the data mining process does not achieve the expected results, it should be tested multiple times in this part until the accuracy is verified before it can enter the data processing of the next part [24]. The mined knowledge can be used to solve current problems or applied to new data sets in order to achieve more intelligent data analysis functions [25]. Therefore, a data mining process is usually iterative.


## 2.1.1. Data Cleaning

There is a temporal gap between the specific data that are provided in the real data world and the specific data that are provided in the traditional analysis and data mining [12, 13]. The purpose of data gap cleaning is mainly to reduce the direct impact of these gaps on the final results of a data mining experiment [14]. The main operational techniques for cleaning up data gaps are usually: timely correction of some missing noise values, processing of smooth noise data, and temporal correction of inconsistent noise data [15].


## 2.1.2. Data Integration

When the massive data you receive from a data center mostly come from multiple types of database repositories, you often need a source of data warehouse integrators to help us quickly complete the effective integration of this data [16]. Data analysis integration technology helps effectively reduce duplication and inconsistency of information in the later results analysis data set, which greatly improves the efficiency of later results data analysis and retrieval [17, 18].


## 2.1.3. Data Reduction

When data for mining come from data sources stored in different structures and the volume of data is very large, data mining technology faces enormous challenges. This is where data reduction techniques can help [19]. Given the integrity of the source data, data reduction techniques can be generalized based on the common characteristics of the data set. This extraction technique helps complete data set reduction to reduce the data size and ensure the accuracy of extraction results.


## 2.1.4. Data Transformation

Different data mining algorithms are suitable for processing different types of data, so certain transformations are required for data that does not meet the mining requirements [20]. The main operation strategies of data attribute transformation are smoothing (i.e., data noise can be removed), attribute data construction, normalization, and discrete normalization (or, e.g., using the original age-specific base value and labels in the range 0–5, for a total of 8–15 alternatives). The transformation mode of the database can directly make the comprehensive mining process of massive data more effective, and the working mode of mining is easier to understand [21].


## 2.1.5. Data Mining

In the main step of data mining, you must choose according to the structural characteristics of the data itself and the data functions that the user expects to achieve. The corresponding pattern algorithm is used to automatically extract some implicit data patterns from these data. The optional pattern algorithm types include various types of related data rules, classification, clustering, regression data analysis, decision trees, formula rediscovery, neural networks, and web data mining, each of which focuses on comprehensive analysis and analysis of various data structures from different perspectives data mining [22].


## 2.1.6. Data Presentation

Each link of the data mining process is closely related, and grasping the accuracy of each link has a non-negligible impact on the mining results [23]. If the front part of the data mining process does not achieve the expected results, it should be tested multiple times in this part until the accuracy is verified before it can enter the data processing of the next part [24]. The mined knowledge can be used to solve current problems or applied to new data sets in order to achieve more intelligent data analysis functions [25]. Therefore, a data mining process is usually iterative.


## 2.2. Common Mining Algorithms for Association Rules

The main purpose of rule information mining in affiliated enterprises is to comprehensively evaluate them by using the two main interest degree evaluation indexes of support and confidence, so how to accurately construct two interest degree evaluations of association rules. Indicators are also an important research content of current research on association rule information mining technology, that is, in our real life. One of the main research objectives of rule information mining in affiliated enterprises is how to find exactly which kind of rule information or which combination of information is the most interesting and useful and how to provide enterprise managers with important data and information.


### 2.2.1. Clustering andK-Means Algorithm

Cluster analysis is a process of dividing a data clustering object hierarchy into multiple classes or clusters so that the heights of data objects in the same cluster type have a high degree of similarity, and the heights of data objects in different cluster types are also different. They are a kind of comprehensive learning without process supervision. The cluster analysis algorithm can be roughly subdivided into cluster analysis algorithms based on the hierarchical division clustering method, hierarchical classification method, and density comparison method, among whichk-means clustering is a clustering method based on hierarchical partitioning. It selects p or p data object centers from p or n data objects as the initial data clustering object center and assigns it to the Euclidean time-distance calculation formula. The nearest initial clustering object center calculates the average density of each data clustering object in each cluster using a calculation formula and repeatedly superimposes the hierarchical calculation, comparison, and hierarchical classification until the center after the initial clustering no longer changes. The p and m in are the starting point of n data objects, and m is the average density of each data object in each cluster c.(1)Px=fn+1ωμκn+1!x−x0n+1,C=∑k=0nxkk!+mnX.


### 2.2.2. Apriori Algorithm

Apriori is an algorithm that is one of the most influential alternative mining algorithms for layer-by-layer association data rules. This mining algorithm is based on a superimposed alternative structure idea, also known as a layer-by-layer data search. Denoting the item sets asl and l1, then l1 is used to generate l1 and l2, respectively, and then l2 to generate l2 and l3; repeat this iterative process until you cannot find more frequent item set lists. It needs one scan in the database every time it searches layer by layer. In each data scan, it only needs to consider a frequent k item sets with the same time width function k. The traditional apriori algorithm uses a support-confidence framework to evaluate the value of association rules, but in practical applications, relying only on the limits of support and confidence thresholds will still result in some useless or even wrong association rules. Apriori algorithm often uses the calculation formula shown in the following equations:(2)L=∫x2nx+xn+1n+1+C,(3)FV=VANd1−e−rtDNd2.


### 2.2.3. FP-Growth Algorithm

The FP-growth algorithm compresses, filters, and mines databases and stores them to form frequent pattern trees. The nodes of the frequent pattern tree have four fields. The first field stores the name of the project. The second field is the number of transactions sharing this prefix. The third field is a link to the name of the next public project in another path. This pointer is called the “link pointer.” The link pointer links all nodes with the same project name in different paths. The fourth field is a list of pointers. Each pointer points to a child node that shares the same prefix as the current node. The item title table contains all items sorted by frequency of occurrence. Each item has a pointer to the first node in the tree with the same item name. You can access this pointer and other link pointers in the tree together. All nodes with the same name are now available. To build frequent pattern trees, you need to read the database twice. Initially, the frequent pattern tree has only one node named “empty” that is, the root node, which does not represent an item in the database. During the second read of the database, each item is converted to a node in the frequent pattern tree, and each transaction is converted to a path in the frequent pattern tree.(4)Fa=a−1stebucstiona∗t+a+1bxvsstna∗t,Sp=∑j=1vMfD∗log2MfD+JV.


## 2.2.1. Clustering andK-Means Algorithm

Cluster analysis is a process of dividing a data clustering object hierarchy into multiple classes or clusters so that the heights of data objects in the same cluster type have a high degree of similarity, and the heights of data objects in different cluster types are also different. They are a kind of comprehensive learning without process supervision. The cluster analysis algorithm can be roughly subdivided into cluster analysis algorithms based on the hierarchical division clustering method, hierarchical classification method, and density comparison method, among whichk-means clustering is a clustering method based on hierarchical partitioning. It selects p or p data object centers from p or n data objects as the initial data clustering object center and assigns it to the Euclidean time-distance calculation formula. The nearest initial clustering object center calculates the average density of each data clustering object in each cluster using a calculation formula and repeatedly superimposes the hierarchical calculation, comparison, and hierarchical classification until the center after the initial clustering no longer changes. The p and m in are the starting point of n data objects, and m is the average density of each data object in each cluster c.(1)Px=fn+1ωμκn+1!x−x0n+1,C=∑k=0nxkk!+mnX.


## 2.2.2. Apriori Algorithm

Apriori is an algorithm that is one of the most influential alternative mining algorithms for layer-by-layer association data rules. This mining algorithm is based on a superimposed alternative structure idea, also known as a layer-by-layer data search. Denoting the item sets asl and l1, then l1 is used to generate l1 and l2, respectively, and then l2 to generate l2 and l3; repeat this iterative process until you cannot find more frequent item set lists. It needs one scan in the database every time it searches layer by layer. In each data scan, it only needs to consider a frequent k item sets with the same time width function k. The traditional apriori algorithm uses a support-confidence framework to evaluate the value of association rules, but in practical applications, relying only on the limits of support and confidence thresholds will still result in some useless or even wrong association rules. Apriori algorithm often uses the calculation formula shown in the following equations:(2)L=∫x2nx+xn+1n+1+C,(3)FV=VANd1−e−rtDNd2.


## 2.2.3. FP-Growth Algorithm

The FP-growth algorithm compresses, filters, and mines databases and stores them to form frequent pattern trees. The nodes of the frequent pattern tree have four fields. The first field stores the name of the project. The second field is the number of transactions sharing this prefix. The third field is a link to the name of the next public project in another path. This pointer is called the “link pointer.” The link pointer links all nodes with the same project name in different paths. The fourth field is a list of pointers. Each pointer points to a child node that shares the same prefix as the current node. The item title table contains all items sorted by frequency of occurrence. Each item has a pointer to the first node in the tree with the same item name. You can access this pointer and other link pointers in the tree together. All nodes with the same name are now available. To build frequent pattern trees, you need to read the database twice. Initially, the frequent pattern tree has only one node named “empty” that is, the root node, which does not represent an item in the database. During the second read of the database, each item is converted to a node in the frequent pattern tree, and each transaction is converted to a path in the frequent pattern tree.(4)Fa=a−1stebucstiona∗t+a+1bxvsstna∗t,Sp=∑j=1vMfD∗log2MfD+JV.


## 3. Design and Application of the Evaluation System

### 3.1. Improvement of Association Rule Algorithm

Association rule mining algorithms are mainly used to determine the association process of redundant and non-critical data in the database according to the importance of attributes. The idea of the association rule mining algorithm is more important in the attribute method of data mining theory. This method saves a lot of search space and at the same time obtains the best or near-optimal reduction set. Association rule mining algorithms can also be used in combination with many attribute reduction algorithms for big data mining. Find the core attribute set according to the generated discriminant matrix and use the association rule heuristic method to find the minimum reduction set or core. Basically, add attributes according to the importance of other attributes until the knowledge gained is the same as the classification ability of the original decision table:The first step is to determine whether the decision table is a compatible table; if it is incompatible, use the improved discernibility matrix algorithm; otherwise, use the original discernibility matrix algorithm.The next stepB = COREC (D) stores all the individual attribute elements in the searched decision table matrix. The calculation formula for the mapping of set B is shown in the following equation:(5)Bu=∑j=0XCnjuji−ui2∑j=0XCnjdji2,whereB(u) represents the number of elements of the reduced set and u is the matrix operation factor.


### 3.2. Construction of the Quality Evaluation Index System

When establishing a simple rating problem, indicators can be placed side by side in the same layer, but when complex rating problems arise, the rating indicator system should be layered, and indicators between different levels have a subordinate relationship. Since the indicators are independent of each other, the rating system has a tree structure. In view of the many aspects of the overall quality of college students, we will use a graded scoring system to establish a student quality index system. After reading a lot of literature, interviewing university counselors, and surveying university teachers and students, a comprehensive quality evaluation index system for students was established based on the five principles of establishing a quality evaluation model. The index system of college students is shown in Table1.Table 1
Index system for college students.
NameFirst-level indicatorsSecondary indicatorsThird-level indicatorsCollege students’ quality evaluation model means standard systemIdeological character,A1Political quality,B11Political attitude,C111Political theory level,C112Knowledge competence,A2Main course score,B21Time attendance,C121Reward and punishment situation,C122Physical and mental health,A3Psychological quality,B32Public class score,C211Professional course score,C212Practical innovation ability,A4Social activity ability,B41Sports results,C311Social practice ability,C411


### 3.3. Design Idea of the Evaluation System

The design concepts of the evaluation system are as follows: (1) establish an online student quality evaluation system suitable for the interactive interface and realize the quality evaluation network office to scientifically, standardly, and efficiently calculate and evaluate the results. (2) The student quality assessment model can be dynamically adjusted; the assessment model can be updated at any time according to policy changes or expert suggestions; the assessment indicators can be changed; and the indicator weights can be different according to expert guidance and data analysis. It can be modified to adapt to evolving social talent standards and educational concepts. (3) Share data with existing school education and office systems (including a complete student management system) to avoid data waste, cumbersome data import, and error-prone problems. We provide each student with fair, just, and accurate quality assessment results; they can query, print, and export the results. (4) Various statistical analyses can be carried out for each grade, and by displaying icons, the statistical analysis results can be more easily understood.


### 3.4. G1 Method Level Analysis

The G1 method is mainly to sort each adjacent index according to its degree of importance and then based on the quantitative relationship of the index importance in order to separate the adjacent indexes for comprehensive comparison because the test for poor consistency is itself tedious. In order to effectively avoid the test of poor consistency, the adjacent indexes are no longer compared, and the average weight of each index evaluation-related index can be determined by calculating a mathematical quantitative relationship for all the comparison index values. Therefore, it can be seen that the core step of the G1 method is to rank the indicators according to their importance, and this sorting method based on multiexpert evaluation can use the set-valued iterative method.The steps of the G1 method are as follows: first, the order relationship of the importance of the indicators is determined according to the set straight iteration method. There areN experts who select s (1 ≤ s ≤ n) of the most important indexes among m indexes X = {x1, x2, …, xm} at the same level; then the choice of the n-th expert is X (n) = {x1(n), x2(n), …, xs(n)}. Because the level of experts and knowledge experience are different, each expert is given a weight (λ1, λ2, …, λn). Then the n-th expert has the i-th choice for the indicator in X; there are two cases in this expert’s choice X(n) or not in X(n), and the relative importance of the indicator in X is as follows:(6)Xn=L∗snb∑i=1nlyin−fyiyl+yln+∑im+1nl+nRyin−fyiyR+yRn.


### 3.5. Analysis of the Quality Evaluation Model of College Students

The complete student quality assessment model has completeness, importance, and simplicity. First, it can comprehensively and scientifically reflect the comprehensive quality level of students from different levels and angles, thereby ensuring the scientific and completeness of quality assessment. Then, considering the completeness, we assign different weights to each layer of the indicator. To emphasize this point again, the qualitative and quantitative organic combination of basic indicators is very useful for data collection and calculation; finally, the hierarchical index system is comprehensive. Not only will you get good evaluation results, but you will also be able to understand the evaluation of each layer of the indicator because, even within the same layer or multiple layers, there may be different internal links between indicators, suitable for data analysis and mining. Table2 is the weight table of student quality evaluation indicators.Table 2
Student weight evaluation index weight table.
Project nameWeight coefficientSecondary weight coefficientThird-level weight coefficientMoral quality,A10.23650.31480.4866Knowledge competence,A20.21870.33520.5241Physical and mental health,A30.22570.40280.5582Practical innovation ability,A40.23960.36570.5739


### 3.6. Data Collection Creation

Take the comprehensive qualities of students in the college student assessment library as an example to formally describe the association rules related to assessment. Let the original item set defined by the left part {p1, p2, pn} on the right side with the rule set implication be a and b and the original item set defined by the middle part {q1, q2, qm} on the right side be a and h. They can all be a subset of the original subset i. Let g = h ∪ b, which means that there is a polynomial set that supports both one for h and two for b. Users can usually define two new thresholds at the same time, which requires that the data support and data credibility of the analysis rules generated by the data mining analysis system should not exceed a threshold given by us.


## 3.1. Improvement of Association Rule Algorithm

Association rule mining algorithms are mainly used to determine the association process of redundant and non-critical data in the database according to the importance of attributes. The idea of the association rule mining algorithm is more important in the attribute method of data mining theory. This method saves a lot of search space and at the same time obtains the best or near-optimal reduction set. Association rule mining algorithms can also be used in combination with many attribute reduction algorithms for big data mining. Find the core attribute set according to the generated discriminant matrix and use the association rule heuristic method to find the minimum reduction set or core. Basically, add attributes according to the importance of other attributes until the knowledge gained is the same as the classification ability of the original decision table:The first step is to determine whether the decision table is a compatible table; if it is incompatible, use the improved discernibility matrix algorithm; otherwise, use the original discernibility matrix algorithm.The next stepB = COREC (D) stores all the individual attribute elements in the searched decision table matrix. The calculation formula for the mapping of set B is shown in the following equation:(5)Bu=∑j=0XCnjuji−ui2∑j=0XCnjdji2,whereB(u) represents the number of elements of the reduced set and u is the matrix operation factor.


## 3.2. Construction of the Quality Evaluation Index System

When establishing a simple rating problem, indicators can be placed side by side in the same layer, but when complex rating problems arise, the rating indicator system should be layered, and indicators between different levels have a subordinate relationship. Since the indicators are independent of each other, the rating system has a tree structure. In view of the many aspects of the overall quality of college students, we will use a graded scoring system to establish a student quality index system. After reading a lot of literature, interviewing university counselors, and surveying university teachers and students, a comprehensive quality evaluation index system for students was established based on the five principles of establishing a quality evaluation model. The index system of college students is shown in Table1.Table 1
Index system for college students.
NameFirst-level indicatorsSecondary indicatorsThird-level indicatorsCollege students’ quality evaluation model means standard systemIdeological character,A1Political quality,B11Political attitude,C111Political theory level,C112Knowledge competence,A2Main course score,B21Time attendance,C121Reward and punishment situation,C122Physical and mental health,A3Psychological quality,B32Public class score,C211Professional course score,C212Practical innovation ability,A4Social activity ability,B41Sports results,C311Social practice ability,C411


## 3.3. Design Idea of the Evaluation System

The design concepts of the evaluation system are as follows: (1) establish an online student quality evaluation system suitable for the interactive interface and realize the quality evaluation network office to scientifically, standardly, and efficiently calculate and evaluate the results. (2) The student quality assessment model can be dynamically adjusted; the assessment model can be updated at any time according to policy changes or expert suggestions; the assessment indicators can be changed; and the indicator weights can be different according to expert guidance and data analysis. It can be modified to adapt to evolving social talent standards and educational concepts. (3) Share data with existing school education and office systems (including a complete student management system) to avoid data waste, cumbersome data import, and error-prone problems. We provide each student with fair, just, and accurate quality assessment results; they can query, print, and export the results. (4) Various statistical analyses can be carried out for each grade, and by displaying icons, the statistical analysis results can be more easily understood.


## 3.4. G1 Method Level Analysis

The G1 method is mainly to sort each adjacent index according to its degree of importance and then based on the quantitative relationship of the index importance in order to separate the adjacent indexes for comprehensive comparison because the test for poor consistency is itself tedious. In order to effectively avoid the test of poor consistency, the adjacent indexes are no longer compared, and the average weight of each index evaluation-related index can be determined by calculating a mathematical quantitative relationship for all the comparison index values. Therefore, it can be seen that the core step of the G1 method is to rank the indicators according to their importance, and this sorting method based on multiexpert evaluation can use the set-valued iterative method.The steps of the G1 method are as follows: first, the order relationship of the importance of the indicators is determined according to the set straight iteration method. There areN experts who select s (1 ≤ s ≤ n) of the most important indexes among m indexes X = {x1, x2, …, xm} at the same level; then the choice of the n-th expert is X (n) = {x1(n), x2(n), …, xs(n)}. Because the level of experts and knowledge experience are different, each expert is given a weight (λ1, λ2, …, λn). Then the n-th expert has the i-th choice for the indicator in X; there are two cases in this expert’s choice X(n) or not in X(n), and the relative importance of the indicator in X is as follows:(6)Xn=L∗snb∑i=1nlyin−fyiyl+yln+∑im+1nl+nRyin−fyiyR+yRn.


## 3.5. Analysis of the Quality Evaluation Model of College Students

The complete student quality assessment model has completeness, importance, and simplicity. First, it can comprehensively and scientifically reflect the comprehensive quality level of students from different levels and angles, thereby ensuring the scientific and completeness of quality assessment. Then, considering the completeness, we assign different weights to each layer of the indicator. To emphasize this point again, the qualitative and quantitative organic combination of basic indicators is very useful for data collection and calculation; finally, the hierarchical index system is comprehensive. Not only will you get good evaluation results, but you will also be able to understand the evaluation of each layer of the indicator because, even within the same layer or multiple layers, there may be different internal links between indicators, suitable for data analysis and mining. Table2 is the weight table of student quality evaluation indicators.Table 2
Student weight evaluation index weight table.
Project nameWeight coefficientSecondary weight coefficientThird-level weight coefficientMoral quality,A10.23650.31480.4866Knowledge competence,A20.21870.33520.5241Physical and mental health,A30.22570.40280.5582Practical innovation ability,A40.23960.36570.5739


## 3.6. Data Collection Creation

Take the comprehensive qualities of students in the college student assessment library as an example to formally describe the association rules related to assessment. Let the original item set defined by the left part {p1, p2, pn} on the right side with the rule set implication be a and b and the original item set defined by the middle part {q1, q2, qm} on the right side be a and h. They can all be a subset of the original subset i. Let g = h ∪ b, which means that there is a polynomial set that supports both one for h and two for b. Users can usually define two new thresholds at the same time, which requires that the data support and data credibility of the analysis rules generated by the data mining analysis system should not exceed a threshold given by us.


## 4. Results and Discussion of Association Rule Algorithm

The college student quality assessment system can query and count the historical data of student quality assessment and can realize the functions of generating individual and class horizontal and vertical comparison analysis charts, quality ability distribution maps, and so on. And use the association rules FP-FBS mining algorithm proposed in this paper, data mining on the evaluation indicators, and evaluation results; generate association rules; and provide reference analysis for instructors and other managers. At the same time, the student quality evaluation model can be dynamically adjusted, and the evaluation model can be updated at any time according to policy changes or expert suggestions, and the evaluation indicators can be changed to adapt to the evolving social talent standards and education concepts. The relevant data of college student quality assessment are shown in Table3.Table 3
Relevant data for quality assessment of college students.
NumberingCategoryRangesProportion (%)WeightsI1Excellent90 points or more24.90.5322I2Good80–90 points35.20.4915I3General60–80 points41.80.4472I4Pass60 points or less31.60.6203According to the standards for the assessment and evaluation of the management system of college students’ quality in China established in this paper, enter the data of the assessment group of the college students’ school quality every month and conduct it in the data correlation and rule data miner and algorithm analysis model of its assertive. Select decision trees and unclassified algorithm models and so on and conduct relevant data analysis and training to generate decision trees. After introducing the decision tree generated by data classification analysis rules and massive data analysis into the algorithm, a decision tree for automatically predicting the academic performance level of candidates is automatically optimized. It can be clearly seen that among the group of students with excellent comprehensive ability and quality, 52.16% have excellent undergraduate comprehensive level and 45.13% have a good comprehensive group. The two types have a total of 81.29%, which has certain absolute advantages. Therefore, not only can we analyze and accurately predict a junior high school student with a good comprehensive psychological quality, but the comprehensive academic level must also be excellent. After importing the intelligent prediction result tree generated by data analysis into the algorithm through the data classification analysis rules and comprehensive training method, what the automatic program generates is the intelligent prediction result tree used to automatically predict the comprehensive ability of students. The statistical results of college students’ quality evaluation under the association rule mining algorithm are shown in Figure2.Figure 2
Statistical results of college students’ quality evaluation under association rule mining algorithm.From the data in Figure2, it can be seen that under the association rule mining algorithm, the group of college students with excellent overall qualities accounted for 52.16% of the high school students and 45.13% of the good ones. The two categories accounted for 81.29%.Research has shown that the association rule algorithms can be used to derive appropriate association rules. The rule style between innovation style and innovation quality is medium => medium innovation (support 52.4%, confidence 78.6%). This rule has the highest support and confidence. The strong rule correlation shows that there is a strong intrinsic relationship between the two qualities—style and innovation. The study showed that the use of associative rule extraction algorithms in assessing the quality of college students’ knowledge can improve the accuracy of the assessment results. The relevant data are shown in Figure3.Figure 3
The effect of association rule extraction algorithm on improving evaluation efficiency.The data in Figure3 shows that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33%. This shows that the algorithm in this paper can improve the accuracy of the evaluation efficiency.In this paper, the use of data mining technology in the analysis of college students’ quality assessment data is presented in detail, and the rules and knowledge of mining are explained and analyzed. We prove that associative rule mining algorithm is suitable for analyzing college students’ quality assessment data. Thus, the research and application of the system have played an effective role in helping to create and improve a comprehensive quality intervention system for college students, and it is a very good technical support platform. Many practical problems were identified in the operation and use of the system, including the lack of a highly automated data preprocessing process and the inability to adapt to large databases. The study showed that the associative rule extraction algorithm can reduce the probability of outliers in the college student assessment system. The relevant data are shown in Figure4.Figure 4
Association rule mining algorithm can reduce the probability of deviation in college student quality assessment system.From the data in Figure4, it can be seen that the association rule mining algorithm can reduce the deviation in the college student quality evaluation system, and the occurrence probability of deviation in the calculation process is reduced by 27%.The association algorithm rules data mining; the association algorithm is based on the relational table model in the task database as the data basis, mining the task-related sample data with key associations and forming the basic relationship table that is relevant to the task. Based on the basic relationship table, the basic attribute data are classified and comprehensively generalized to find out the basic attributes closely related to the task decision-makers and rulemaking, and a sample analysis model that can be subdivided is constructed and analyzed. At this time, the model collection in the data association model rule data mining model algorithm is an effective association data model collection optimized by function compression or using functional. Starting from the comprehensive qualities of college students, a quality assessment system is established on the basis of the association rule algorithm, which provides the guarantee of information circulation for the overall development and improvement of students. The association rule mining algorithm applied to the college student quality evaluation system is helpful for students to find their own problems and make relevant improvements to ensure the comprehensiveness of the students’ comprehensive quality information. The relevant data is shown in Figure5.Figure 5
The effect of association rule mining algorithm on students’ comprehensive quality information.It can be seen from Figure5 that the association rule mining algorithm applied to the college students’ quality assessment system increased the possibility of students discovering their deficiencies by 15.8%, and the comprehensiveness of college students’ comprehensive qualities increased by 22%. Therefore, it is proved that the algorithm can guarantee the comprehensiveness of the comprehensive quality information of college students.


## 5. Conclusions

(1)
An association rule mining algorithm is a mathematical algorithm used to find correlations between different things and is often used to extract relevant knowledge from high school student learning data. Algorithms for extracting associative rules for data analysis and their applications are very wide. Currently, the most commonly used algorithms for association function rule retrieval are thek-means algorithm, apriori algorithm, and P-FP-GROWTHRS algorithm. The practical application of various associative rule retrieval and data analysis algorithms in the psychological comprehensive quality assessment system of college students helps college students discover their psychological problems as early as possible, and it is great support for the gradual improvement of the comprehensive psychological quality of Chinese college students.(2)
The results of the study show that the use of associative rule mining algorithms to assess the quality of college students can improve the test efficiency by 24%, increase the accuracy of the test results by 33%, and reduce the probability of outliers in the calculation process by 27%. It can be seen that an associative rule mining algorithm can be applied to college student quality assessment system and at the same time provide a barrier to the accuracy and efficiency of quality assessment results.(3)
The study found that under the association rule mining algorithm, the group with excellent comprehensive qualities of college students accounted for 52.16% of excellent middle school students and 45.13% of good ones. The two categories accounted for 81.29%. The association rule mining algorithm applied to the college students’ quality evaluation system increased the possibility of students discovering their deficiencies by 15.8%, and the comprehensiveness of college students’ comprehensive qualities increased by 22%.The shortcoming of this paper is that the test data of the association rule algorithm is not comprehensive and extensive, and it is recommended to further implement the test of the algorithm.



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*Source: 6721504-2022-04-21.xml*