# The Application of Multi-Source Big Data Mining Techniques in the Analysis of Basketball Economic Management

Authors: Hui Liang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China’s basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study.


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## Body

## 1. Introduction

With the changing consumer attitudes of society as a whole, there are many opportunities and challenges for the economic development [1, 2]. When a sport becomes an industry, it reflects not only the preferences of the masses, but also the inclusiveness of the Chinese market economy. We should seize the opportunity to attach great importance to the basketball industry, which has great power, and actively promote the development of the basketball industry to meet the needs of the people for sports culture and social change. As long as the basketball industry is suited to the development of the socialist economy, it will certainly have a better future. However, compared to the advanced basketball court model abroad, there are still many problems in China’s basketball industry. The comparison clearly shows that China’s sports industry is too concentrated in terms of market supply distribution, the financing channel is too single, and the agency mechanism of market operation is not perfect.The 21st century is an era of basic stability in the world landscape, and under the influence of the development of the socialist market economy, the sports industry has become an important part of the tertiary industry [3–5]. It is also a new growth point for China’s economic growth, and the promotion of the sports industry is also a need for the construction of China’s socialist market economy system. In recent years, China’s support for the basketball industry has influenced the development of domestic sporting industry, and in 2004, a change in the system of play to a home and away game with a north-south division only allowed the professionalization of basketball to take another step forward, thus demonstrating that the political environment and political thinking has laid an effective foundation for the development of China’s basketball industry. And, as the sport becomes more and more sophisticated, the competition allows for the introduction of foreign aids, creating a live atmosphere, and thus attracting a larger audience.From its inception to its development and prosperity, the NBA has gradually formed a scientific management system, from optimizing the game to the selection of outstanding athletes, all have a scientific and reasonable management mechanism [6]. The high income of athletes and coaches has deeply stimulated the competition in the industry, which cannot be achieved just by optimizing the events and the selection of outstanding athletes. The NBA league has formed a complete and orderly operation mechanism in the construction of mobile arenas for athletes, and it is even advertising revenue and a series of other NBA-related products. This powerful operating mechanism is beyond the reach of the industrial development of Chinese sports. This is a big step forward for the basketball industry in China, but compared to the excellent management system of the NBA, China’s economic development has started late and the political aspect has an important impact on the development of the industrialization of basketball. The development of China’s sporting industry has been affected by the planned economy, and there are obvious loopholes in the management system and the system in the process of transformation, which has seriously hindered the development of China’s sporting industry and consequently the economic development [7–10].The difference between the NBA and Chinese professional basketball in terms of the principal-agent mechanism is that they operate either on a league basis or on a club level, where they are able to secure the investment capital of the principal and the expertise of the agent. By effectively combining management capabilities, the league is able to generate effective revenue through its market operations. The NBA Board of Directors is the supreme authority of the club. The Board of Directors has the power to make decisions, and the relationship between the Board of Directors and the various departments is one of employment, with a clear division of functions and work between the various departments. Such a corporate organizational structure is able to produce highly efficient management results, thus enabling NBA’s market to expand. In terms of financing capacity analysis, the NBA has a monopoly on talent funding and marketing for professional basketball in the United States. All bring great market wealth to the NBA in the USA; the market brings business opportunities to the sport of basketball and wins wealth for the market [11, 12].Due to their public sector role, sports associations in China still operate in an administrative mode and remain a government function in terms of management [13, 14]. Chinese clubs are nominally market players but in practice are subject to strict administrative management and discipline. Compared to the professional sports market in the US, Chinese professional sports have a strong administrative character in terms of operation, and are only a semi-market mechanism. For the economic management science of resource allocation to analyze the distribution of benefits, clubs do not have the right to dispose of league revenues, which, to a certain extent, violates the basic principles of the market. For interviews with basketball players, very few were satisfied with this distribution system, indicating that there are certain problems with the benefit distribution system in China. China has not developed a selection system for the best players and is not professional enough to select the best talent in the clubs, resulting in an uneven distribution of talent between the clubs, thus causing a large gap in the competitive strength between the clubs.The current operation of China’s professional basketball league is characterized by an over-concentrated market supply distribution, a single financing channel for the supplying body, a lack of overall market scale, and a lack of a proper proxy mechanism for market operation, as well as a lack of market management and service awareness and irregularities in the operation of the market body. Although many scholars have analysed the basketball industry [15], few studies have been conducted specifically from economics, combining economic theory with the industrialization of basketball as a sport. Most of the studies are based on foreign experience and are not sufficiently linked to the specific situation of China. This paper draws on economic theory to collect multiple factors influencing the industrialization of the basketball economy through multi-source big data mining techniques, and uses algorithms to mine the correlation between the influencing factors in the industrialization of the basketball economy. The results of association rule mining are used to analyze the key influencing factors and to analyze the relationships between the industrialization of the basketball economy. Exploring the current situation and the problems that arise in the industrialization of basketball in China is more relevant for the development of the industry.


## 2. Multi-Source Big Data Mining Technology

There are many influencing factors in basketball economic management. How to establish the association relationship between each influencing factor and extract the important factors influencing basketball economic management from many factors is the key to develop measures to improve the basketball economic industrialization. To this end, this part of the research uses the unsupervised learning algorithm (Apriori) [16–18] in machine learning to establish association rules between each influencing factor and obtain the key factors affecting the basketball economic management by analyzing the strong association rules, which provides a basis for the subsequent formulation of measures to improve the industrialization of the basketball economy.


### 2.1. The Apriori Association Rule Mining Algorithm

#### 2.1.1. Principle of the Apriori Algorithm

Apriori is a common data mining algorithm that is implemented through unsupervised learning. The process does not require human involvement and can be done independently by the computer. This type of algorithm is mainly used to analyze datasets with unknown categories or attributes, where the relationships between the data cannot be annotated manually due to the lack of a priori knowledge.The core of the Apriori algorithm is a two-stage recursive mining process, where the first stage is to find all combinations of factors that appear frequently in the dataset, and the second stage is to find association rules that satisfy the requirements from all frequent items in the set. Therefore, the two-stage recursive process involves two important concepts, namely, minimum support and minimum confidence, which need to be set artificially before the association rules are mined. The smaller the preset minimum support, the more frequent items are retained, the more association rules can be extracted, and the wider the range of values for the confidence of association rules, but it will greatly prolong the running time of the program. Therefore, it is necessary to set a reasonable minimum confidence level to constrain the confidence value of the association rule. The formula for calculating support and confident is shown in the following equation.(1)supportA⟶B=PA∪B,confidentA⟶B=PA∪BPA.where support indicates the probability of event A and B occurring simultaneously, and confident indicates the probability of event B occurring simultaneously given that event A occurs.There are two main steps in the process of scanning frequent itemsets using the Apriori algorithm: concatenation and pruning. In the join process, the Apriori algorithm exhausts all the itemsets in the transaction set by iterating through them layer by layer, i.e., searching forK+1 itemsets using K itemsets. The pruning process is intersected with the joining process in order to discard the itemsets that do not satisfy the minimum support and to retain the frequent itemsets. The principle of the process is as follows: all non-empty subsets of frequent itemsets are frequent, and supersets of infrequent itemsets must be infrequent. For example, if the set of items containing factors A and B is infrequent, then any superset containing A and B, such as A,B,C,A,B,F,Gis infrequent and can be discarded in the course of the operation to reduce the program’s computing time.After the Apriori algorithm finds all frequent itemsets, different association rules can be extracted based on each frequent itemset, and all association rules that do not meet the minimum confidence level are discarded, and the retained association rules are the result of the Apriori algorithm. In this process, the high-dimensional association rules are generated based on the low-dimensional association rules, which is equivalent to a conditional complement to the accuracy of the low-dimensional association rules, and the association rules that satisfy both minimum support and minimum confidence are called strong association rules.


#### 2.1.2. The Process of Implementing the Apriori Algorithm

The process of implementing Apriori is described here. Firstly, the dataset given in Figure1 contains four data items, namely, {A, B, D}, {B, C, E}, {A, B, C, E}, {B, E}. A minimum support of 2 and a minimum confidence of 0.8 are set to mine the dataset for association rules.Figure 1
Example of the apriori algorithm.As the itemset {D} only appears once in the dataset, the minimum support is 1, which is smaller than the preset minimum support of 2. The itemset is discarded and all the itemsets that satisfy the minimum support are kept as frequent itemsets.From the principle of Apriori algorithm, it can exhaust all the itemsets in the transaction set by iterative search, i.e., usingK itemsets to search K + 1 itemsets. Then, on the basis of the frequent one set, continue to scan the dataset to get all the binomial sets, and similarly, round off the binomial sets that do not meet the minimum support to get the frequent binomial set. The first stage of mining can be completed by iterating in this way until all frequent itemsets are found.For all the frequent itemsets obtained above, the confidence level of each itemset can be calculated to complete the second stage of strong association rule screening. Take the itemset {A, C} as an example, the two confidence levels are calculated as shown in equation (2). Since the minimum confidence level is 0.8, the association rule (A⟶C) is a strong association rule, while the association rule (C⟶A) does not meet the minimum confidence level requirement and needs to be discarded. In this way, the correlation between the factors in the dataset can be analyzed.(2)confidentA⟶C=PA∪CPA=1,confidentC⟶A=PA∪CPC=23.As can be seen from the operations of the Apriori algorithm, there are very obvious drawbacks to the Apriori algorithm: in the case of very large datasets with a particularly large number of factors, the algorithm ends up with a huge number of data items. The mining of association rules can become extremely inefficient and can place a huge burden on the system in which the program is running. Combining other algorithms to improve the mining efficiency of the Apriori algorithm is therefore the most effective way to address this drawback.


### 2.2. Hash Tree-Based Improvement Methods

The biggest advantage of the Hash tree [19, 20] is that it greatly reduces the number of candidate sets and extracts frequent itemsets efficiently. To improve the Apriori’s original binomial set extraction, the Hash tree splits the input set of items by determining whether the leaf node is full or not. The principle of splitting a Hash tree is to insert the original and new itemsets into a new leaf node at the next level, making the original leaf node a non-leaf node. This allows the original itemset to have a separate branch and the other itemsets to share a branch.After all frequent binomials have been extracted using the Hash tree, subsequent frequent itemsets are still extracted using the Apriori algorithm. In this case, due to the small number of itemsets contained in each leaf node, the Apriori algorithm is perfectly adequate and there is no need to use the Hash tree again. Due to the complexity of the Hash tree algorithm itself, its continued use in subsequent operations would instead reduce the efficiency of the frequent itemsets. Therefore, Hash trees are only used in the first few layers of the Apriori algorithm.


## 2.1. The Apriori Association Rule Mining Algorithm

### 2.1.1. Principle of the Apriori Algorithm

Apriori is a common data mining algorithm that is implemented through unsupervised learning. The process does not require human involvement and can be done independently by the computer. This type of algorithm is mainly used to analyze datasets with unknown categories or attributes, where the relationships between the data cannot be annotated manually due to the lack of a priori knowledge.The core of the Apriori algorithm is a two-stage recursive mining process, where the first stage is to find all combinations of factors that appear frequently in the dataset, and the second stage is to find association rules that satisfy the requirements from all frequent items in the set. Therefore, the two-stage recursive process involves two important concepts, namely, minimum support and minimum confidence, which need to be set artificially before the association rules are mined. The smaller the preset minimum support, the more frequent items are retained, the more association rules can be extracted, and the wider the range of values for the confidence of association rules, but it will greatly prolong the running time of the program. Therefore, it is necessary to set a reasonable minimum confidence level to constrain the confidence value of the association rule. The formula for calculating support and confident is shown in the following equation.(1)supportA⟶B=PA∪B,confidentA⟶B=PA∪BPA.where support indicates the probability of event A and B occurring simultaneously, and confident indicates the probability of event B occurring simultaneously given that event A occurs.There are two main steps in the process of scanning frequent itemsets using the Apriori algorithm: concatenation and pruning. In the join process, the Apriori algorithm exhausts all the itemsets in the transaction set by iterating through them layer by layer, i.e., searching forK+1 itemsets using K itemsets. The pruning process is intersected with the joining process in order to discard the itemsets that do not satisfy the minimum support and to retain the frequent itemsets. The principle of the process is as follows: all non-empty subsets of frequent itemsets are frequent, and supersets of infrequent itemsets must be infrequent. For example, if the set of items containing factors A and B is infrequent, then any superset containing A and B, such as A,B,C,A,B,F,Gis infrequent and can be discarded in the course of the operation to reduce the program’s computing time.After the Apriori algorithm finds all frequent itemsets, different association rules can be extracted based on each frequent itemset, and all association rules that do not meet the minimum confidence level are discarded, and the retained association rules are the result of the Apriori algorithm. In this process, the high-dimensional association rules are generated based on the low-dimensional association rules, which is equivalent to a conditional complement to the accuracy of the low-dimensional association rules, and the association rules that satisfy both minimum support and minimum confidence are called strong association rules.


### 2.1.2. The Process of Implementing the Apriori Algorithm

The process of implementing Apriori is described here. Firstly, the dataset given in Figure1 contains four data items, namely, {A, B, D}, {B, C, E}, {A, B, C, E}, {B, E}. A minimum support of 2 and a minimum confidence of 0.8 are set to mine the dataset for association rules.Figure 1
Example of the apriori algorithm.As the itemset {D} only appears once in the dataset, the minimum support is 1, which is smaller than the preset minimum support of 2. The itemset is discarded and all the itemsets that satisfy the minimum support are kept as frequent itemsets.From the principle of Apriori algorithm, it can exhaust all the itemsets in the transaction set by iterative search, i.e., usingK itemsets to search K + 1 itemsets. Then, on the basis of the frequent one set, continue to scan the dataset to get all the binomial sets, and similarly, round off the binomial sets that do not meet the minimum support to get the frequent binomial set. The first stage of mining can be completed by iterating in this way until all frequent itemsets are found.For all the frequent itemsets obtained above, the confidence level of each itemset can be calculated to complete the second stage of strong association rule screening. Take the itemset {A, C} as an example, the two confidence levels are calculated as shown in equation (2). Since the minimum confidence level is 0.8, the association rule (A⟶C) is a strong association rule, while the association rule (C⟶A) does not meet the minimum confidence level requirement and needs to be discarded. In this way, the correlation between the factors in the dataset can be analyzed.(2)confidentA⟶C=PA∪CPA=1,confidentC⟶A=PA∪CPC=23.As can be seen from the operations of the Apriori algorithm, there are very obvious drawbacks to the Apriori algorithm: in the case of very large datasets with a particularly large number of factors, the algorithm ends up with a huge number of data items. The mining of association rules can become extremely inefficient and can place a huge burden on the system in which the program is running. Combining other algorithms to improve the mining efficiency of the Apriori algorithm is therefore the most effective way to address this drawback.


## 2.1.1. Principle of the Apriori Algorithm

Apriori is a common data mining algorithm that is implemented through unsupervised learning. The process does not require human involvement and can be done independently by the computer. This type of algorithm is mainly used to analyze datasets with unknown categories or attributes, where the relationships between the data cannot be annotated manually due to the lack of a priori knowledge.The core of the Apriori algorithm is a two-stage recursive mining process, where the first stage is to find all combinations of factors that appear frequently in the dataset, and the second stage is to find association rules that satisfy the requirements from all frequent items in the set. Therefore, the two-stage recursive process involves two important concepts, namely, minimum support and minimum confidence, which need to be set artificially before the association rules are mined. The smaller the preset minimum support, the more frequent items are retained, the more association rules can be extracted, and the wider the range of values for the confidence of association rules, but it will greatly prolong the running time of the program. Therefore, it is necessary to set a reasonable minimum confidence level to constrain the confidence value of the association rule. The formula for calculating support and confident is shown in the following equation.(1)supportA⟶B=PA∪B,confidentA⟶B=PA∪BPA.where support indicates the probability of event A and B occurring simultaneously, and confident indicates the probability of event B occurring simultaneously given that event A occurs.There are two main steps in the process of scanning frequent itemsets using the Apriori algorithm: concatenation and pruning. In the join process, the Apriori algorithm exhausts all the itemsets in the transaction set by iterating through them layer by layer, i.e., searching forK+1 itemsets using K itemsets. The pruning process is intersected with the joining process in order to discard the itemsets that do not satisfy the minimum support and to retain the frequent itemsets. The principle of the process is as follows: all non-empty subsets of frequent itemsets are frequent, and supersets of infrequent itemsets must be infrequent. For example, if the set of items containing factors A and B is infrequent, then any superset containing A and B, such as A,B,C,A,B,F,Gis infrequent and can be discarded in the course of the operation to reduce the program’s computing time.After the Apriori algorithm finds all frequent itemsets, different association rules can be extracted based on each frequent itemset, and all association rules that do not meet the minimum confidence level are discarded, and the retained association rules are the result of the Apriori algorithm. In this process, the high-dimensional association rules are generated based on the low-dimensional association rules, which is equivalent to a conditional complement to the accuracy of the low-dimensional association rules, and the association rules that satisfy both minimum support and minimum confidence are called strong association rules.


## 2.1.2. The Process of Implementing the Apriori Algorithm

The process of implementing Apriori is described here. Firstly, the dataset given in Figure1 contains four data items, namely, {A, B, D}, {B, C, E}, {A, B, C, E}, {B, E}. A minimum support of 2 and a minimum confidence of 0.8 are set to mine the dataset for association rules.Figure 1
Example of the apriori algorithm.As the itemset {D} only appears once in the dataset, the minimum support is 1, which is smaller than the preset minimum support of 2. The itemset is discarded and all the itemsets that satisfy the minimum support are kept as frequent itemsets.From the principle of Apriori algorithm, it can exhaust all the itemsets in the transaction set by iterative search, i.e., usingK itemsets to search K + 1 itemsets. Then, on the basis of the frequent one set, continue to scan the dataset to get all the binomial sets, and similarly, round off the binomial sets that do not meet the minimum support to get the frequent binomial set. The first stage of mining can be completed by iterating in this way until all frequent itemsets are found.For all the frequent itemsets obtained above, the confidence level of each itemset can be calculated to complete the second stage of strong association rule screening. Take the itemset {A, C} as an example, the two confidence levels are calculated as shown in equation (2). Since the minimum confidence level is 0.8, the association rule (A⟶C) is a strong association rule, while the association rule (C⟶A) does not meet the minimum confidence level requirement and needs to be discarded. In this way, the correlation between the factors in the dataset can be analyzed.(2)confidentA⟶C=PA∪CPA=1,confidentC⟶A=PA∪CPC=23.As can be seen from the operations of the Apriori algorithm, there are very obvious drawbacks to the Apriori algorithm: in the case of very large datasets with a particularly large number of factors, the algorithm ends up with a huge number of data items. The mining of association rules can become extremely inefficient and can place a huge burden on the system in which the program is running. Combining other algorithms to improve the mining efficiency of the Apriori algorithm is therefore the most effective way to address this drawback.


## 2.2. Hash Tree-Based Improvement Methods

The biggest advantage of the Hash tree [19, 20] is that it greatly reduces the number of candidate sets and extracts frequent itemsets efficiently. To improve the Apriori’s original binomial set extraction, the Hash tree splits the input set of items by determining whether the leaf node is full or not. The principle of splitting a Hash tree is to insert the original and new itemsets into a new leaf node at the next level, making the original leaf node a non-leaf node. This allows the original itemset to have a separate branch and the other itemsets to share a branch.After all frequent binomials have been extracted using the Hash tree, subsequent frequent itemsets are still extracted using the Apriori algorithm. In this case, due to the small number of itemsets contained in each leaf node, the Apriori algorithm is perfectly adequate and there is no need to use the Hash tree again. Due to the complexity of the Hash tree algorithm itself, its continued use in subsequent operations would instead reduce the efficiency of the frequent itemsets. Therefore, Hash trees are only used in the first few layers of the Apriori algorithm.


## 3. Data Preparation for Association Rule Mining

### 3.1. Selection of Data for Basketball Economic Management

There are many factors that influence the industrial development of basketball in China. Firstly, the political system (PS) has an intuitively important role in the development of the sport for all. Secondly, the degree of economic development (ED), the income level of the population (IITP), and the amount of free time (AFT) all have an important impact on the development of the industrialization of the basketball. Intrinsic factors such as the construction of sports grounds (TCSG), the establishment of basketball programmes (BC), the development of interest in basketball (IB), and the sporting talent of the whole population (STA) are also factors that cannot be ignored in the development of the industrialization of basketball. Extrinsic factors such as ICC, SP, IN, PC, and LE must also be taken into account. As the Apriori algorithm cannot analyze association rules for continuous data, the data to be analyzed is here discretised for the purposes of the study.


### 3.2. Discrete Processing of Basketball Economic Management Data

A transaction set for association rule mining is obtained by transforming the data in the dataset according to the hierarchy in the table. The level of data in the dataset is represented as a transaction item I, which can be expressed as I = {PSi, EDi, IITPi, AFTi, TCSGi, BCi, IBi, STAi, ICCi, SPi, INi, PCi, LEi}, withi being the level of each factor. Based on the conversion rules, all the data items in the dataset were converted into transaction items to obtain the transaction set for association rule analysis, and a total of 1015 sets of transaction items were obtained. At this point, the transaction set is brought into the Apriori algorithm to extract the frequent items set and generate association rules.


### 3.3. Association Rule Mining for Basketball Economic Management Influences

The Apriori algorithm is used to determine the minimum confidence level for the economic management of basketball. In general, rules with a confidence level greater than 0.75 are referred to as useful rules in association rule analysis, and the minimum confidence level is generally greater than 0.75, so the minimum confidence level is set to 0.8 in this study. In addition, to ensure the comprehensiveness and reliability of the association rule mining results, we need to pay special attention to the number of association rules containing LE1 and LE4. In addition, to ensure the comprehensiveness and reliability of the association rule mining results, we need to pay particular attention to the number of association rules containing LE1 and LE4, and we know that when the support degree is greater than 0.02, we can obtain two-dimensional association rules containing LE4, but we cannot obtain association rules containing LE1. However, when the support is less than or equal to 0.02, the number of association rules containing LE1 remains stable at 8, so the minimum support is set to 0.02.For all the association rules mined by the Apriori algorithm, there are 13 factors used for association rule analysis in this paper, and the mining results can theoretically generate association rules in 13 dimensions. By analyzing the relationship between the association rules in each dimension, it is found that no one-dimensional association rules appear in the association rule mining results, and all basketball economic factors are covered from the four-dimensional association rules onwards. From the five-dimensional association rules onwards, a large number of invalid association rules appeared. In this study, the analysis focused on two-, three- and four-dimensional association rules. It is worth mentioning that the presence of invalid association rules directly affects the accuracy of the analysis results and should be eliminated during the analysis. For example, in the analysis of three-dimensional association rules, if the confidence level of the two-dimensional association rule {PC1, PS1}⟶ IE1 is 0.8333, and the confidence level of the three-dimensional association rule {PC1, PS1, IN1}⟶ IE1 is also 0.8333, then the three-dimensional association rule is judged as an invalid association rule and needs to be eliminated.


## 3.1. Selection of Data for Basketball Economic Management

There are many factors that influence the industrial development of basketball in China. Firstly, the political system (PS) has an intuitively important role in the development of the sport for all. Secondly, the degree of economic development (ED), the income level of the population (IITP), and the amount of free time (AFT) all have an important impact on the development of the industrialization of the basketball. Intrinsic factors such as the construction of sports grounds (TCSG), the establishment of basketball programmes (BC), the development of interest in basketball (IB), and the sporting talent of the whole population (STA) are also factors that cannot be ignored in the development of the industrialization of basketball. Extrinsic factors such as ICC, SP, IN, PC, and LE must also be taken into account. As the Apriori algorithm cannot analyze association rules for continuous data, the data to be analyzed is here discretised for the purposes of the study.


## 3.2. Discrete Processing of Basketball Economic Management Data

A transaction set for association rule mining is obtained by transforming the data in the dataset according to the hierarchy in the table. The level of data in the dataset is represented as a transaction item I, which can be expressed as I = {PSi, EDi, IITPi, AFTi, TCSGi, BCi, IBi, STAi, ICCi, SPi, INi, PCi, LEi}, withi being the level of each factor. Based on the conversion rules, all the data items in the dataset were converted into transaction items to obtain the transaction set for association rule analysis, and a total of 1015 sets of transaction items were obtained. At this point, the transaction set is brought into the Apriori algorithm to extract the frequent items set and generate association rules.


## 3.3. Association Rule Mining for Basketball Economic Management Influences

The Apriori algorithm is used to determine the minimum confidence level for the economic management of basketball. In general, rules with a confidence level greater than 0.75 are referred to as useful rules in association rule analysis, and the minimum confidence level is generally greater than 0.75, so the minimum confidence level is set to 0.8 in this study. In addition, to ensure the comprehensiveness and reliability of the association rule mining results, we need to pay special attention to the number of association rules containing LE1 and LE4. In addition, to ensure the comprehensiveness and reliability of the association rule mining results, we need to pay particular attention to the number of association rules containing LE1 and LE4, and we know that when the support degree is greater than 0.02, we can obtain two-dimensional association rules containing LE4, but we cannot obtain association rules containing LE1. However, when the support is less than or equal to 0.02, the number of association rules containing LE1 remains stable at 8, so the minimum support is set to 0.02.For all the association rules mined by the Apriori algorithm, there are 13 factors used for association rule analysis in this paper, and the mining results can theoretically generate association rules in 13 dimensions. By analyzing the relationship between the association rules in each dimension, it is found that no one-dimensional association rules appear in the association rule mining results, and all basketball economic factors are covered from the four-dimensional association rules onwards. From the five-dimensional association rules onwards, a large number of invalid association rules appeared. In this study, the analysis focused on two-, three- and four-dimensional association rules. It is worth mentioning that the presence of invalid association rules directly affects the accuracy of the analysis results and should be eliminated during the analysis. For example, in the analysis of three-dimensional association rules, if the confidence level of the two-dimensional association rule {PC1, PS1}⟶ IE1 is 0.8333, and the confidence level of the three-dimensional association rule {PC1, PS1, IN1}⟶ IE1 is also 0.8333, then the three-dimensional association rule is judged as an invalid association rule and needs to be eliminated.


## 4. Experimental Analysis

### 4.1. Association Rule Mining Results

Apriori algorithm based on Hash Tree was mined to obtain a total of 189 association rules, including 60 association rules with LE1 as the posterior term, 20 association rules with LE2 as the posterior term, 100 association rules with LE3 as the posterior term, and 9 association rules with LE4 as the posterior term. LE1 represents a manual efficiency class of less than 0.3, LE2 represents a manual efficiency class between 0.3 and 0.6, LE3 represents a manual efficiency class between 0.6 and 0.9, and LE4 represents an accident risk class greater than 0.9. Table1 shows a total of 60 association rules with the latter term being LE1. There are 8 two-dimensional association rules, 44 three-dimensional association rules, and 8 four-dimensional association rules.Table 1
LE1 association rules.
Pre-itemBack itemsConfidencePS1, ED2LE10.875ITTP2, BC1LE10.8333ED2, BC1LE11TSCG1, IB2LE11STA2, AFT1LE10.8333ICC2, BC1LE10.875SP1, IN2LE10.8333PC1, LE2LE11BC2, AFT3, ED3LE10.875IN3, IB2, PC1LE10.8333ED1, AFT2, IB3LE11PC3, ICC2, IITP1LE10.8333A comparison of the confidence levels of the antecedents in the LE1 association rule is shown in Figure2. As can be seen from Figure 2, the confidence levels of the priors in the LE1 association rule do not differ significantly compared to each other, indicating the validity of the data selected.Figure 2
Confidence comparison of the former term in the LE1 association rule.Table2 shows the LE2 association rules for the latter term, with a total of 9 rules. Among them, 3 are two-dimensional association rules, 6 are three-dimensional association rules, and all four-dimensional association rules are invalid association rules.Table 2
LE2 association rules.
Pre-itemBack itemsConfidencePS2, AFT1LE21AFT2, IB4LE21IN2, PC3LE20.916PS4, AFT4, BC2LE20.916BC2, STA1, PC4LE20.909ED2, IB4, SP3LE20.9AFT2, IB4, ICC4LE20.833TSCG2, PS1, SP4LE20.818ICC3, IN4, PC4LE20.818A comparison of the confidence levels of each antecedent term with the posterior term being the LE2 association rule is shown in Figure3. From this radar plot, it can be seen that the maximum confidence level of the antecedent term is 1 and the minimum confidence level is 0.818. The confidence levels vary considerably between the different antecedents and the four-dimensional association rule is considered invalid here.Figure 3
Comparison of confidence levels for each antecedent of the LE2 association rule.


### 4.2. Analysis of Association Rules

The key factors influencing the economic management of basketball were extracted by counting the frequency of the occurrence of each factor in the association rule. The results of the statistical analysis of the two-dimensional association rule are shown in Figure4. Through the analysis, it was found that the ranks of four factors, ED, IITP, basketball course establishment BC, and basketball interest cultivation IB, all change with the rank at different levels, and there is no interruption between successive change levels, so it can be seen that these four factors are the four key factors affecting the economic management of basketball.Figure 4
Statistical frequencies of two-dimensional association rules for (a) LE1 and (b) LE2.
(a)(b)In addition, it can be seen from Figure4 that factors such as personnel cooperation proficiency SP and free time AFT also change with rank, but there are interruptions between ranks and it is impossible to tell whether they are key factors. The remaining factors do not show similar patterns, and then higher dimensional association rules need to be analyzed to further determine them.The statistical frequencies of the three-dimensional association rules for LE3 and LE4 are shown in Tables3 and 4.Table 3
Statistical frequency of the three-dimensional association rules for LE3.
CategoryFrequency (%)ED38IITP38AFT313TCSG38BC317IB313STA24IN48PC321Table 4
Statistical frequencies of LE4 three-dimensional association rules.
CategoryFrequency (%)IN317PC232ICC217IB417ED117The comparison of the statistical frequencies of LE3 and LE4 three-dimensional association rules is shown in Figure5. From the information in the figure, it can be seen that the proportion of PC3 in LE3 is the largest and its corresponding frequency is also the highest. The proportion of PC2 in LE4 is the largest and its corresponding frequency is also the largest, both of which indicate that the frequency occupied by PC is the largest.Figure 5
Results of statistical analysis of three-dimensional association rules (a) LE3 three-dimensional association rule statistical frequency (b) Statistical frequency of LE4 three-dimensional association rules.
(a)(b)The results of the analysis found trends in ED, IITP, AFT, and BC in the three-dimensional correlation rule further demonstrating the relevance of these four factors to basketball economic management. In addition to this, further analysis of the relevance of IB and PC to basketball economic management, the study found that the way in which IB is present in the different levels indicates that it is not a key factor affecting basketball economic management. Further analysis of the trend in PC was still not able to determine this, then the analysis of higher dimensional rules would need to be continued to determine this.In addition to this, STA was found to be a potential key factor, and as this pattern was not present in the two-dimensional association rules, it will need to be further identified in the 4-dimensional association rules. The remaining factors did not show a pattern of variation with rank in either the two-dimensional or three-dimensional association rules, so it was determined that they were not key influences. Detailed data on the statistical frequencies of the LE1 four-dimensional association rules are shown in Table5.Table 5
Detailed statistical frequency data for the LE1 four-dimensional association rule.
CategoryFrequency (%)IB16IB23AFT319PC113BC43IITP39STA213ED13PC39SP23I N43ICC29TCSG23STA13A visual comparison of the detailed statistical frequency data for the LE1 four-dimensional association rule is shown in Figure6. Figure 6 shows that the frequencies of the categories in LE1 are relatively low, with the largest frequency being 19% and the smallest frequency being 3% and the frequencies of the influencing factors are relatively evenly distributed.Figure 6
Comparison of LE1 four-dimensional association rule statistical frequency data.The results of the four-dimensional association rule statistical analysis are shown in Figure6, which shows that STA is not an influential factor in the economic management of basketball. IB appears in the same way in the four-dimensional association rule as in the two-dimensional and three-dimensional association rules, because the number of association rules with LE2 as the latter term is less than the number of LE1 as the latter term, and this paper suggests that basketball interest development IB is also an important factor in the economic management of basketball.LE2 four-dimensional association rule statistical frequency and LE3 Four-dimensional association rule statistical frequency data are shown in Tables6 and 7.Table 6
LE2 four-dimensional association rule statistical frequency data.
CategoryFrequency (%)PC48IB212STA215SP218AFT422ICC125Table 7
LE3 statistical frequency data for four-dimensional association rules.
CategoryFrequency (%)TSCG110PC112STA315SP318BC420IN125A comparison of the statistical frequency data for the LE2 and LE3 four-dimensional association rules is shown in Figure7.Figure 7
Comparison of statistical frequency data for LE2 and LE3 four-dimensional association rules (a) LE2 (b) LE3.
(a)(b)As can be seen in Figure7, the statistical frequency data of the four-dimensional association rules of LE2 and LE3 are relatively evenly distributed and show a linear relationship with the increasing data, indicating that the extracted important influencing factors play an important role in the economic management of basketball.


## 4.1. Association Rule Mining Results

Apriori algorithm based on Hash Tree was mined to obtain a total of 189 association rules, including 60 association rules with LE1 as the posterior term, 20 association rules with LE2 as the posterior term, 100 association rules with LE3 as the posterior term, and 9 association rules with LE4 as the posterior term. LE1 represents a manual efficiency class of less than 0.3, LE2 represents a manual efficiency class between 0.3 and 0.6, LE3 represents a manual efficiency class between 0.6 and 0.9, and LE4 represents an accident risk class greater than 0.9. Table1 shows a total of 60 association rules with the latter term being LE1. There are 8 two-dimensional association rules, 44 three-dimensional association rules, and 8 four-dimensional association rules.Table 1
LE1 association rules.
Pre-itemBack itemsConfidencePS1, ED2LE10.875ITTP2, BC1LE10.8333ED2, BC1LE11TSCG1, IB2LE11STA2, AFT1LE10.8333ICC2, BC1LE10.875SP1, IN2LE10.8333PC1, LE2LE11BC2, AFT3, ED3LE10.875IN3, IB2, PC1LE10.8333ED1, AFT2, IB3LE11PC3, ICC2, IITP1LE10.8333A comparison of the confidence levels of the antecedents in the LE1 association rule is shown in Figure2. As can be seen from Figure 2, the confidence levels of the priors in the LE1 association rule do not differ significantly compared to each other, indicating the validity of the data selected.Figure 2
Confidence comparison of the former term in the LE1 association rule.Table2 shows the LE2 association rules for the latter term, with a total of 9 rules. Among them, 3 are two-dimensional association rules, 6 are three-dimensional association rules, and all four-dimensional association rules are invalid association rules.Table 2
LE2 association rules.
Pre-itemBack itemsConfidencePS2, AFT1LE21AFT2, IB4LE21IN2, PC3LE20.916PS4, AFT4, BC2LE20.916BC2, STA1, PC4LE20.909ED2, IB4, SP3LE20.9AFT2, IB4, ICC4LE20.833TSCG2, PS1, SP4LE20.818ICC3, IN4, PC4LE20.818A comparison of the confidence levels of each antecedent term with the posterior term being the LE2 association rule is shown in Figure3. From this radar plot, it can be seen that the maximum confidence level of the antecedent term is 1 and the minimum confidence level is 0.818. The confidence levels vary considerably between the different antecedents and the four-dimensional association rule is considered invalid here.Figure 3
Comparison of confidence levels for each antecedent of the LE2 association rule.


## 4.2. Analysis of Association Rules

The key factors influencing the economic management of basketball were extracted by counting the frequency of the occurrence of each factor in the association rule. The results of the statistical analysis of the two-dimensional association rule are shown in Figure4. Through the analysis, it was found that the ranks of four factors, ED, IITP, basketball course establishment BC, and basketball interest cultivation IB, all change with the rank at different levels, and there is no interruption between successive change levels, so it can be seen that these four factors are the four key factors affecting the economic management of basketball.Figure 4
Statistical frequencies of two-dimensional association rules for (a) LE1 and (b) LE2.
(a)(b)In addition, it can be seen from Figure4 that factors such as personnel cooperation proficiency SP and free time AFT also change with rank, but there are interruptions between ranks and it is impossible to tell whether they are key factors. The remaining factors do not show similar patterns, and then higher dimensional association rules need to be analyzed to further determine them.The statistical frequencies of the three-dimensional association rules for LE3 and LE4 are shown in Tables3 and 4.Table 3
Statistical frequency of the three-dimensional association rules for LE3.
CategoryFrequency (%)ED38IITP38AFT313TCSG38BC317IB313STA24IN48PC321Table 4
Statistical frequencies of LE4 three-dimensional association rules.
CategoryFrequency (%)IN317PC232ICC217IB417ED117The comparison of the statistical frequencies of LE3 and LE4 three-dimensional association rules is shown in Figure5. From the information in the figure, it can be seen that the proportion of PC3 in LE3 is the largest and its corresponding frequency is also the highest. The proportion of PC2 in LE4 is the largest and its corresponding frequency is also the largest, both of which indicate that the frequency occupied by PC is the largest.Figure 5
Results of statistical analysis of three-dimensional association rules (a) LE3 three-dimensional association rule statistical frequency (b) Statistical frequency of LE4 three-dimensional association rules.
(a)(b)The results of the analysis found trends in ED, IITP, AFT, and BC in the three-dimensional correlation rule further demonstrating the relevance of these four factors to basketball economic management. In addition to this, further analysis of the relevance of IB and PC to basketball economic management, the study found that the way in which IB is present in the different levels indicates that it is not a key factor affecting basketball economic management. Further analysis of the trend in PC was still not able to determine this, then the analysis of higher dimensional rules would need to be continued to determine this.In addition to this, STA was found to be a potential key factor, and as this pattern was not present in the two-dimensional association rules, it will need to be further identified in the 4-dimensional association rules. The remaining factors did not show a pattern of variation with rank in either the two-dimensional or three-dimensional association rules, so it was determined that they were not key influences. Detailed data on the statistical frequencies of the LE1 four-dimensional association rules are shown in Table5.Table 5
Detailed statistical frequency data for the LE1 four-dimensional association rule.
CategoryFrequency (%)IB16IB23AFT319PC113BC43IITP39STA213ED13PC39SP23I N43ICC29TCSG23STA13A visual comparison of the detailed statistical frequency data for the LE1 four-dimensional association rule is shown in Figure6. Figure 6 shows that the frequencies of the categories in LE1 are relatively low, with the largest frequency being 19% and the smallest frequency being 3% and the frequencies of the influencing factors are relatively evenly distributed.Figure 6
Comparison of LE1 four-dimensional association rule statistical frequency data.The results of the four-dimensional association rule statistical analysis are shown in Figure6, which shows that STA is not an influential factor in the economic management of basketball. IB appears in the same way in the four-dimensional association rule as in the two-dimensional and three-dimensional association rules, because the number of association rules with LE2 as the latter term is less than the number of LE1 as the latter term, and this paper suggests that basketball interest development IB is also an important factor in the economic management of basketball.LE2 four-dimensional association rule statistical frequency and LE3 Four-dimensional association rule statistical frequency data are shown in Tables6 and 7.Table 6
LE2 four-dimensional association rule statistical frequency data.
CategoryFrequency (%)PC48IB212STA215SP218AFT422ICC125Table 7
LE3 statistical frequency data for four-dimensional association rules.
CategoryFrequency (%)TSCG110PC112STA315SP318BC420IN125A comparison of the statistical frequency data for the LE2 and LE3 four-dimensional association rules is shown in Figure7.Figure 7
Comparison of statistical frequency data for LE2 and LE3 four-dimensional association rules (a) LE2 (b) LE3.
(a)(b)As can be seen in Figure7, the statistical frequency data of the four-dimensional association rules of LE2 and LE3 are relatively evenly distributed and show a linear relationship with the increasing data, indicating that the extracted important influencing factors play an important role in the economic management of basketball.


## 5. Conclusion

With the gradual development of basketball towards industrialization, the need for economic management of basketball has become increasingly strong. There are many factors that influence the economic management of basketball, and the identification of the most critical factors that influence the economic management of basketball is the key to developing appropriate improvement measures. In this paper, we use the Hash Tree Apriori algorithm to analyze the correlation between various economic data and the management of basketball industrialization. Before the association rule analysis, the factors in the dataset were firstly classified into levels, and then the dataset was discretised according to the level classification criteria to complete the preparation of the transaction set for association rule mining. After association rule mining, the results of the analysis showed that economic development (ED), population income level (IITP), free time (AFT), basketball interest development (IB), and basketball course establishment (BC) were the five key factors influencing the economic management of experimental basketball, and an increase in economic development, population income level, free time, and basketball course establishment would all contribute to the basketball economic management analysis. This paper makes targeted observations on the impact of basketball economic management science analysis through the five key factors that have been mined, but as there are more extraneous factors that interfere with practical applications, the impact of more factors will be considered next to achieve more accurate data mining research.



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*Source: 5362900-2022-06-28.xml*