# Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Authors: Shujun Hou
Journal: Mobile Information Systems
 (2021)

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## Abstract

The advent of the information age has changed every existing career and revolutionized most if not all fields, notwithstanding many benefits that came along with it. There has been an exponential rise in information and, alongside it, an increase in data. Data centers have erupted with details as the number of rows in databases grows by the day. The use of technology has nevertheless become essential in many company models and organizations, warranting its usage in virtually every channel. College physical education and sports are not an exception as students studying such subjects are skyrocketing. As the information is getting more complex, improved methods are needed to research and analyze data. Fortunately, data mining has come to the rescue. Data mining is a collection of analytical methods and procedures used exclusively for the sake of data extraction. It may be used to analyze features and trends from vast quantities of data. The objective of this study is to explore the use of data mining technologies in the analysis of college students’ sports psychology. This study uses clustering methods for the examination of sports psychology. We utilize three clustering methods for this aim: expectation-maximization (EM) algorithm, k-means, COBWEB, density-based clustering of applications with noise (DBSCAN), and agglomerative hierarchal clustering algorithms. We perform our forecasts based on various metrics combined with the past outcomes of college sports using these methods. In contrast to conventional data research and analysis techniques, our approaches have relatively high prediction accuracy as far as college athletics is concerned.


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## Body

## 1. Introduction

Data mining is the “extraction” from an extensive dataset containing confidential information. Datasets have hidden data related to the features and trends in various datasets that may be “mined” by data mining methods. In data, there is essential information concealed. This knowledge is frequently buried and unused since the underlying data are produced more quickly than they can be analyzed and made sense of. Individuals or organizations with limited resources–particularly technical—will find it almost difficult to locate and get any insight from the data.The term “data mining” refers to a set of tools and methods for “extracting” or “mining” information from vast quantities of data [1]. It is all about identifying patterns and connections in data that may lead to new understanding. Furthermore, these connections may serve as forecasters of future events.Data mining’s significance has been demonstrated for commercial applications, criminal investigations, biomedicine [2], and more recently, counter-terrorism [3–5]. For example, most merchants utilize data mining techniques to discover consumer purchasing trends. https://www.Amazon.com analyzes purchase history to offer product suggestions to customers. Data mining may be used wherever there is a large amount of data that needs to be explored.Machine learning (ML), mathematical algorithms, and statistical models are a part of data mining technologies. In many areas, such as companies, financial institutions, and governmental organizations, ML tools were extensively utilized. To name just a few, RSA for secure information transfer, mathematical tools, and methods from such fields as a set, graph, and number theory have been used for public-key cryptosystems. Some of the techniques that are extremely important in data mining are the patterns of the tracking of aberrations in datasets, which are used at regular intervals; classification of data attributes to indistinguishable categories to derive or use further information for the same purpose, clustering the grouping of concerning data chunks. Figure1 is a representation of the classification versus clustering presentation.Figure 1
Classification vs. clustering.Figure1 indicates the differences between the clustering and classification techniques of data mining. Classification techniques have predefined outputs, whereas clustering creates subgroups based on the characteristics of the datasets.This research mainly deals with the clustering technique and its algorithms to investigate the application of the data mining technology in sports in higher institutions of learning in China. Clustering is slightly similar to classification as they are both employed in pattern recognition in ML. However, classification uses predefined classes, whereas clustering identifies similarities between objects and “clusters” based on those similarities. Table1 indicates the main differences between classification and clustering algorithms.Table 1
Differences between classification and clustering data mining techniques.
No.ClassificationClustering1Labeled input dataUnlabeled input data2Predetermined outputUnknown output3Employs supervised learningUses unsupervised learning4Trained datasets are used to produce the different classificationsPrepared datasets not used to create the clusters5More complexLess complex6Two-phaseSingle-phaseThe single-phase and low complexities make clustering easier to employ in studying college data mining applications, which is why we selected it for this research.


### 1.1. k-Means Algorithm

k-means is an iterative algorithm that partitions the dataset into distinct predefined and nonoverlapping clusters. In this algorithm, each data point can only belong to a single group. It tries to keep intracluster data points as close as possible while maintaining the clusters as distinct (far) as possible. By assigning data points to a cluster, it can maintain the sum of the cluster's centroid (i.e., the arithmetic mean of all data points belonging to that cluster) and squared distance between the data points.. The lower the variance among groups, the more homogenized (alike) the data points within that cluster are. The algorithm first works by the number of clusters K; centroids are then initialized instead by shuffling the datasets, after which a random selection of K data points is conducted without replacement. Finally, the processes are iterated until there is no further change in the centroids. Computations are performed for the squared distance between the centroids and data points and assigned to the closest centroid. The cluster’s centroids are computed by averaging all the data points belonging to each group. It employs the EM methodology where the E-step assigns the data points to the closest clusters, while the M-step is the computation process of the centroid for each set. Using the k-means cluster analysis aims to organize psychologically relevant parameters of expectations based on individual elements into statistically homogenous cluster groupings [2].Figure2 shows datasets before and after the k-means clustering algorithm application. Datasets with similar characteristics are “clustered”/grouped in the same cluster.Figure 2
k-means clustering process.A significant property of the k-means cluster analysis is that discrepancy between the clusters is reduced due to optimization. Objects in the same group become insignificant, but differences across clusters are noteworthy. Identifying the first cluster centers is necessary to discover minor discrepancies between the indicators under consideration in one group [2, 3]. A study by [6–12] indicated a k-means clustering algorithm's merits over density-based clustering. It is because the density-based clustering does not consider all the data points informing the clusters. The study was conducted using R studio and R programming language to investigate athletes during training sessions. The research results are validated using the k-means algorithm in American football sports over the other traditional approaches [13]. Figure 2 shows the data before and after the application of k-means clustering.


### 1.2. Expectation-Maximization Algorithm

It is an optimization procedure like the gradient-descent algorithm with the advantage that updates can be computed analytically in many circumstances. Its flexibility also places it at a vantage position as compared to the other optimization techniques. The EM algorithm computes a close approximation of the optimal parameters effectively. After the process, a data curve is assigned to the cluster from which it is most likely to originate [14]. It is mainly used on incomplete data or one with missing data points or latent variables. Figure 3 indicates the EM algorithm on a Gaussian curve.Figure 3
Representation of the expectation-maximization algorithm on a Gaussian curve.Figure3 shows a Gaussian curve of density against value for the expectation-maximization algorithm.


### 1.3. DBS

It is a density-based clustering algorithm and slightly similar to the mean-shift algorithm. Its mechanism is to locate high-density regions separated from one another by low-density regions. It checks the epsilonε (local expanding cluster radius) value and groups the closely fitted data points together. It is not a prerequisite to defining the number of clusters; however, the algorithm can identify outliers as noise. Notwithstanding the merits of DBSCAN, it works poorly with groups of varying densities [15]. It might also not work well with high-dimensional data due to the limitations of calculating the epsilon value. Figure 4 is a representation of the DBSCAN algorithm.Figure 4
DBSCAN representation.Figure4 shows the core points, noise points, and border points of the DBSCAN clustering technique. The epsilon value is also indicated as the cluster radius.


### 1.4. COBWEB

It generates the hierarchical clustering. The clusters are probabilistically described. A classification tree’s nodes represent classes (concepts) and are labeled with a probabilistic idea that aggregates the attribute-value distributions of objects categorized under the node [16].Figure5 shows tree nodes with their respective attribute values.Figure 5
COBWEB clustering.This classification tree may be used to forecast the new object’s missing properties or class. Figure5 is the representation of the COBWEB algorithm.


### 1.5. Hierarchical Clustering

It consists of two major groups, the bottom-up and the top-down (divisive). Each cluster in its cluster starts from the top-down. As it falls, the cluster pairs are combined (agglomerated) consecutively [17]. By contrast, the bottom-up begins as a single cluster divided recursively by moving the hierarchy down. A tree is used to illustrate the order of the clusters, and the distance measure was used to evaluate the data points. The clusters may be merged or divided based on the distance between the data points. In contrast to DBSCAN, it is not necessary to define the number of clusters.


## 1.1. k-Means Algorithm

k-means is an iterative algorithm that partitions the dataset into distinct predefined and nonoverlapping clusters. In this algorithm, each data point can only belong to a single group. It tries to keep intracluster data points as close as possible while maintaining the clusters as distinct (far) as possible. By assigning data points to a cluster, it can maintain the sum of the cluster's centroid (i.e., the arithmetic mean of all data points belonging to that cluster) and squared distance between the data points.. The lower the variance among groups, the more homogenized (alike) the data points within that cluster are. The algorithm first works by the number of clusters K; centroids are then initialized instead by shuffling the datasets, after which a random selection of K data points is conducted without replacement. Finally, the processes are iterated until there is no further change in the centroids. Computations are performed for the squared distance between the centroids and data points and assigned to the closest centroid. The cluster’s centroids are computed by averaging all the data points belonging to each group. It employs the EM methodology where the E-step assigns the data points to the closest clusters, while the M-step is the computation process of the centroid for each set. Using the k-means cluster analysis aims to organize psychologically relevant parameters of expectations based on individual elements into statistically homogenous cluster groupings [2].Figure2 shows datasets before and after the k-means clustering algorithm application. Datasets with similar characteristics are “clustered”/grouped in the same cluster.Figure 2
k-means clustering process.A significant property of the k-means cluster analysis is that discrepancy between the clusters is reduced due to optimization. Objects in the same group become insignificant, but differences across clusters are noteworthy. Identifying the first cluster centers is necessary to discover minor discrepancies between the indicators under consideration in one group [2, 3]. A study by [6–12] indicated a k-means clustering algorithm's merits over density-based clustering. It is because the density-based clustering does not consider all the data points informing the clusters. The study was conducted using R studio and R programming language to investigate athletes during training sessions. The research results are validated using the k-means algorithm in American football sports over the other traditional approaches [13]. Figure 2 shows the data before and after the application of k-means clustering.


## 1.2. Expectation-Maximization Algorithm

It is an optimization procedure like the gradient-descent algorithm with the advantage that updates can be computed analytically in many circumstances. Its flexibility also places it at a vantage position as compared to the other optimization techniques. The EM algorithm computes a close approximation of the optimal parameters effectively. After the process, a data curve is assigned to the cluster from which it is most likely to originate [14]. It is mainly used on incomplete data or one with missing data points or latent variables. Figure 3 indicates the EM algorithm on a Gaussian curve.Figure 3
Representation of the expectation-maximization algorithm on a Gaussian curve.Figure3 shows a Gaussian curve of density against value for the expectation-maximization algorithm.


## 1.3. DBS

It is a density-based clustering algorithm and slightly similar to the mean-shift algorithm. Its mechanism is to locate high-density regions separated from one another by low-density regions. It checks the epsilonε (local expanding cluster radius) value and groups the closely fitted data points together. It is not a prerequisite to defining the number of clusters; however, the algorithm can identify outliers as noise. Notwithstanding the merits of DBSCAN, it works poorly with groups of varying densities [15]. It might also not work well with high-dimensional data due to the limitations of calculating the epsilon value. Figure 4 is a representation of the DBSCAN algorithm.Figure 4
DBSCAN representation.Figure4 shows the core points, noise points, and border points of the DBSCAN clustering technique. The epsilon value is also indicated as the cluster radius.


## 1.4. COBWEB

It generates the hierarchical clustering. The clusters are probabilistically described. A classification tree’s nodes represent classes (concepts) and are labeled with a probabilistic idea that aggregates the attribute-value distributions of objects categorized under the node [16].Figure5 shows tree nodes with their respective attribute values.Figure 5
COBWEB clustering.This classification tree may be used to forecast the new object’s missing properties or class. Figure5 is the representation of the COBWEB algorithm.


## 1.5. Hierarchical Clustering

It consists of two major groups, the bottom-up and the top-down (divisive). Each cluster in its cluster starts from the top-down. As it falls, the cluster pairs are combined (agglomerated) consecutively [17]. By contrast, the bottom-up begins as a single cluster divided recursively by moving the hierarchy down. A tree is used to illustrate the order of the clusters, and the distance measure was used to evaluate the data points. The clusters may be merged or divided based on the distance between the data points. In contrast to DBSCAN, it is not necessary to define the number of clusters.


## 2. Method

### 2.1. Statement of the Problem

Sports psychology is a popular topic in college sports and social studies classes. In most instances, descriptive research will be performed with sports clubs differentiated based on structural factors such as size, age, and the number of sports items provided. However, since sports psychology is highly varied and includes many other features, these factors are not always the most apparent. As a result, data mining is being utilized to perform more exploratory studies on the four major problems faced by sports teams. Retaining and recruiting volunteers and attracting young athletes, coaches, and members are among the subjects covered. Thematic analysis is based on the 2007/2008 Sports Development Report (n = 13,068). For each of the four questions, the decision tree is estimated. The findings indicate that, in addition to the club’s size, the percentage of members who participate in social activities and the types of sports offered are significant determinants of the severity of these issues. In football, tennis, shooting, and equestrian teams, specific problems are more prevalent. Future research may be linked to comparing data mining findings in the study of Korean University sports groups.


### 2.2. Sampling and Study Design

College sports performance data can originate from a variety of sources. In-house statistics are the most commonly used approach. During the test, the sports’ participants pedaled by increasing their power based on incremental load steps. The trainer determines the load. A spirometer and a collection of sensors gather physiological information and study their body’s response to the increased effort. Each test delivers two types of information: factual data and dynamic data. Accurate information describes specific details on the user (e.g., sex, age, and weight) and information about the athletic activity, such as the test length. They give the context required to describe athletic performance and compare athletes adequately. In reality, age, gender, and weight variations between two athletes impact how their performances are evaluated and compared. The tested athletes’ age, BMI, and BSA are the accurate data in this research. The body mass index (BMI) is a metric that compares a person’s weight and height. Table2 indicates some of the metrics used in the experiment.Table 2
College sports participants’ characteristics.
Age (yrs.)BMI (kg/m2)BSA (m2)Min14.0017.651.4401st qu19.0021.231.760Median32.0022.651.840Mean32.0022.761.8323rd qu41.0024.241.910Max61.0033.262.130Table2 shows the sampled sports students. Their ages ranged from 14 to 61 years, BMI ranges were 17.65 to 33.26 kg/m2, and BSA ranged from 1.440 to 2.130 m2.The different heterogeneous physiological signals acquired using sensors, such as heart rate, ventilation, oxygen consumption, carbon dioxide, and oxygen concentration, provide dynamic data. Physiological signals are captured during the test, and each sample provides a snapshot of the observed athlete’s state. A descriptive study was conducted in association with the NCAA basketball program from China. The study was conducted in China college institutions, where a random sampling process was completed. To track the performance of the college basketball team, Catapult Sports OPTIMEYE S5 was worn by all the participants [1]. This device is designed to be worn on the upper-back side of the player’s shoulder pads and to communicate wirelessly via GPS. The player’s movements are captured via the GPS with an accuracy of 3 meters. The participants were of the male gender for consistency of the results. These participants were of an average height of 1.80 meters and had an average weight of 80.29 kg.


### 2.3. Data Preprocessing

Data cleaning and transformation were conducted on the collected data. Data cleaning was performed to remove missing and irrelevant data points. For missing data, the “ignore tuple” technique was employed. The method was selected due to the large dataset used in the research. A combination of binning and regression methods was used. For the binning process, the data were first sorted and then divided into portions of equal size. Each segment was handled separately afterward. Some data segments were replaced with their mean or boundary values. For the regression method, a regression function was used to perform the fitting process. Data transformation which converts data into a suitable form for data mining was conducted [18]. Dimensionality reduction was shown on the data to reduce the data size by encoding mechanisms. Both principal component analysis (PCA) and wavelet transforms were conducted.


### 2.4. Data Analysis

#### 2.4.1. k-Means Clustering

Unlike DBSCAN and hierarchical clustering techniques, the k-means algorithm has a prerequisite to determine the number of clusters. For this process, the elbow method was performed where we parsed multiple values for k and calculated the sum of the inside of squares (WSS). We used Pandas, Numpy, Sklearn, Seaborn, and Matplotlib in k-means clustering. Using the blob data structure, we determined the shape of the data. Figure6 is the python code used for this process.Figure 6
Python code with imports and data characteristics.The elbow process was able to find the optimum number of clusters using the code in Figure7.Figure 7
k-means clustering.Figure8 is a graphical representation of the elbow curve generated from the process.Figure 8
Elbow process graph representation with Matplotlib.Figure8 indicates a graph generated after elbow process application.We then utilize within-sum-of-squares to determine the optimal number of clusters generated for a particular dataset. The total of squared distances between each member of the cluster and its centroid is defined as the inside the sum of squares (WSS) as per the following equation [19]:(1)WSS=∑j=1mxj−cj2,where xjis the datapoint and cj is the point closest to the centroid.


#### 2.4.2. DBSCAN

The epsilon neighborhood of pointp of our dataset D was defined as per equation (1), representing the core regions as Np>minimum number of points:(2)Np=q∈D|distp,q≤ε.Core points are defined as such because they lie on the interior of the cluster. Border points are the ones that lie in the neighborhood of another core point. Noise, on the other hand, represents neighbor border or core points [20]. We employed the Sklearn python library for DBSCAN clustering. After importing the CSV data via the Panda library into a data frame, we dropped all the null values in all the columns and transformed the irrelevant data points. With 3 clusters, the code in Figure 9 was generated using the Matplotlib python library.Figure 9
DBSCAN pictorial representation.Figure9 shows DBSCAN with 3 clusters. The picture was generated via the Matplotlib Python library.


## 2.1. Statement of the Problem

Sports psychology is a popular topic in college sports and social studies classes. In most instances, descriptive research will be performed with sports clubs differentiated based on structural factors such as size, age, and the number of sports items provided. However, since sports psychology is highly varied and includes many other features, these factors are not always the most apparent. As a result, data mining is being utilized to perform more exploratory studies on the four major problems faced by sports teams. Retaining and recruiting volunteers and attracting young athletes, coaches, and members are among the subjects covered. Thematic analysis is based on the 2007/2008 Sports Development Report (n = 13,068). For each of the four questions, the decision tree is estimated. The findings indicate that, in addition to the club’s size, the percentage of members who participate in social activities and the types of sports offered are significant determinants of the severity of these issues. In football, tennis, shooting, and equestrian teams, specific problems are more prevalent. Future research may be linked to comparing data mining findings in the study of Korean University sports groups.


## 2.2. Sampling and Study Design

College sports performance data can originate from a variety of sources. In-house statistics are the most commonly used approach. During the test, the sports’ participants pedaled by increasing their power based on incremental load steps. The trainer determines the load. A spirometer and a collection of sensors gather physiological information and study their body’s response to the increased effort. Each test delivers two types of information: factual data and dynamic data. Accurate information describes specific details on the user (e.g., sex, age, and weight) and information about the athletic activity, such as the test length. They give the context required to describe athletic performance and compare athletes adequately. In reality, age, gender, and weight variations between two athletes impact how their performances are evaluated and compared. The tested athletes’ age, BMI, and BSA are the accurate data in this research. The body mass index (BMI) is a metric that compares a person’s weight and height. Table2 indicates some of the metrics used in the experiment.Table 2
College sports participants’ characteristics.
Age (yrs.)BMI (kg/m2)BSA (m2)Min14.0017.651.4401st qu19.0021.231.760Median32.0022.651.840Mean32.0022.761.8323rd qu41.0024.241.910Max61.0033.262.130Table2 shows the sampled sports students. Their ages ranged from 14 to 61 years, BMI ranges were 17.65 to 33.26 kg/m2, and BSA ranged from 1.440 to 2.130 m2.The different heterogeneous physiological signals acquired using sensors, such as heart rate, ventilation, oxygen consumption, carbon dioxide, and oxygen concentration, provide dynamic data. Physiological signals are captured during the test, and each sample provides a snapshot of the observed athlete’s state. A descriptive study was conducted in association with the NCAA basketball program from China. The study was conducted in China college institutions, where a random sampling process was completed. To track the performance of the college basketball team, Catapult Sports OPTIMEYE S5 was worn by all the participants [1]. This device is designed to be worn on the upper-back side of the player’s shoulder pads and to communicate wirelessly via GPS. The player’s movements are captured via the GPS with an accuracy of 3 meters. The participants were of the male gender for consistency of the results. These participants were of an average height of 1.80 meters and had an average weight of 80.29 kg.


## 2.3. Data Preprocessing

Data cleaning and transformation were conducted on the collected data. Data cleaning was performed to remove missing and irrelevant data points. For missing data, the “ignore tuple” technique was employed. The method was selected due to the large dataset used in the research. A combination of binning and regression methods was used. For the binning process, the data were first sorted and then divided into portions of equal size. Each segment was handled separately afterward. Some data segments were replaced with their mean or boundary values. For the regression method, a regression function was used to perform the fitting process. Data transformation which converts data into a suitable form for data mining was conducted [18]. Dimensionality reduction was shown on the data to reduce the data size by encoding mechanisms. Both principal component analysis (PCA) and wavelet transforms were conducted.


## 2.4. Data Analysis

### 2.4.1. k-Means Clustering

Unlike DBSCAN and hierarchical clustering techniques, the k-means algorithm has a prerequisite to determine the number of clusters. For this process, the elbow method was performed where we parsed multiple values for k and calculated the sum of the inside of squares (WSS). We used Pandas, Numpy, Sklearn, Seaborn, and Matplotlib in k-means clustering. Using the blob data structure, we determined the shape of the data. Figure6 is the python code used for this process.Figure 6
Python code with imports and data characteristics.The elbow process was able to find the optimum number of clusters using the code in Figure7.Figure 7
k-means clustering.Figure8 is a graphical representation of the elbow curve generated from the process.Figure 8
Elbow process graph representation with Matplotlib.Figure8 indicates a graph generated after elbow process application.We then utilize within-sum-of-squares to determine the optimal number of clusters generated for a particular dataset. The total of squared distances between each member of the cluster and its centroid is defined as the inside the sum of squares (WSS) as per the following equation [19]:(1)WSS=∑j=1mxj−cj2,where xjis the datapoint and cj is the point closest to the centroid.


### 2.4.2. DBSCAN

The epsilon neighborhood of pointp of our dataset D was defined as per equation (1), representing the core regions as Np>minimum number of points:(2)Np=q∈D|distp,q≤ε.Core points are defined as such because they lie on the interior of the cluster. Border points are the ones that lie in the neighborhood of another core point. Noise, on the other hand, represents neighbor border or core points [20]. We employed the Sklearn python library for DBSCAN clustering. After importing the CSV data via the Panda library into a data frame, we dropped all the null values in all the columns and transformed the irrelevant data points. With 3 clusters, the code in Figure 9 was generated using the Matplotlib python library.Figure 9
DBSCAN pictorial representation.Figure9 shows DBSCAN with 3 clusters. The picture was generated via the Matplotlib Python library.


## 2.4.1. k-Means Clustering

Unlike DBSCAN and hierarchical clustering techniques, the k-means algorithm has a prerequisite to determine the number of clusters. For this process, the elbow method was performed where we parsed multiple values for k and calculated the sum of the inside of squares (WSS). We used Pandas, Numpy, Sklearn, Seaborn, and Matplotlib in k-means clustering. Using the blob data structure, we determined the shape of the data. Figure6 is the python code used for this process.Figure 6
Python code with imports and data characteristics.The elbow process was able to find the optimum number of clusters using the code in Figure7.Figure 7
k-means clustering.Figure8 is a graphical representation of the elbow curve generated from the process.Figure 8
Elbow process graph representation with Matplotlib.Figure8 indicates a graph generated after elbow process application.We then utilize within-sum-of-squares to determine the optimal number of clusters generated for a particular dataset. The total of squared distances between each member of the cluster and its centroid is defined as the inside the sum of squares (WSS) as per the following equation [19]:(1)WSS=∑j=1mxj−cj2,where xjis the datapoint and cj is the point closest to the centroid.


## 2.4.2. DBSCAN

The epsilon neighborhood of pointp of our dataset D was defined as per equation (1), representing the core regions as Np>minimum number of points:(2)Np=q∈D|distp,q≤ε.Core points are defined as such because they lie on the interior of the cluster. Border points are the ones that lie in the neighborhood of another core point. Noise, on the other hand, represents neighbor border or core points [20]. We employed the Sklearn python library for DBSCAN clustering. After importing the CSV data via the Panda library into a data frame, we dropped all the null values in all the columns and transformed the irrelevant data points. With 3 clusters, the code in Figure 9 was generated using the Matplotlib python library.Figure 9
DBSCAN pictorial representation.Figure9 shows DBSCAN with 3 clusters. The picture was generated via the Matplotlib Python library.


## 3. Methodology Results

We observed that the k-means and EM clustering algorithms beat the others described in the previous sections when we examined the clustering methods utilizing accurate data. Both of these methods are capable of clustering data and defining categories. The COBWEB and DBSCAN methods did not provide the same results. We used a hierarchical technique based on the DTW distance for dynamic data and compared the results to a hierarchy based on the Euclidean distance [21]. The Rand index [6] is used to compare two hierarchical clustering methods.Consequently, when the number of clusters is large, the DTW hierarchical method is essentially similar to the previous technique. If the two tests have different durations, DTW is the better option for determining the distance. Maximum similarity grouping is based on work. The “clustering in cascade” method is employed in the third phase of the load. In this instance, k-means was used to examine the factual data and EM, while hierarchical clustering investigated the DTW distance’s dynamic data.We cluster similarity based on the domain expert’s score to put the findings to the test. The data from domain experts are split into two files. The first file’s Rand index values are all greater than 0.74. The values are about 0.8, mainly for the cascade clustering. It shows that our findings and the vote of domain experts [1] are very similar. The techniques used are effective. When the number of clusters is the same, k-means with hierarchical clustering beats EM with hierarchical clustering. In addition, the hierarchical clustering result is better. The rand index value in the second file is adequate but not as good as before the merge.


## 4. Conclusion

The use of data mining ideas and methods in sports has yet to develop to its full potential. Most sports organizations have just started to uncover facts and knowledge buried in their data in many aspects.We examined an overview of data mining throughout this article and reviewed some of the methods utilized in data mining. We employed the clustering method, which uses an unsupervised learning process for college sports data mining. COBWEB, DBSCAN, k-means, and hierarchical methods are some of the approaches used for clustering. Based on the findings collected, a comparison was made. Because of their consistent results, we suggested k-means and EM algorithms, unlike DBSCAN and COBWEB methods.The research’s subsequent development is to automate classification devices to allocate a new test to the correct cluster to determine maximum workload and final score. Data mining is ubiquitous, and sports at higher learning institutions have long been done. It is thus of most tremendous significance to improve analytical processes via data mining for learning institutions and the sports fraternity.In a word, the data mining technology is a helpful instrument that has been used in many areas and has been successful. Of course, data mining alone is not all-powerful, and its use cannot be isolated from the realistic backdrop. The technology will only have real life by adopting the people-oriented concept. However, the psychological system of sports includes a great deal of valuable knowledge to be found.



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*Source: 6529174-2021-11-23.xml*