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

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shujun Hou
Keyword(s):  
College Students ◽  
Data Mining ◽  
College Athletics ◽  
Data Extraction ◽  
Clustering Algorithms ◽  
Sports Psychology ◽  
Clustering Methods ◽  
Use Of Data ◽  
Use Of Technology ◽  
Density Based Clustering

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.

Evaluation of Clustering Methods for Adaptive Learning Systems

2015 ◽  
pp. 237-260 ◽  
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

Evaluation of Clustering Methods for Adaptive Learning Systems

2016 ◽  
pp. 519-542
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

scholarly journals Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

2021 ◽  
pp. 73-79
Author(s):  
G. Ramadevi ◽  
Srujitha Yeruva ◽  
P. Sravanthi ◽  
P. Eknath Vamsi ◽  
S. Jaya Prakash
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Efficiency Comparison ◽  
Using Data ◽  
Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

scholarly journals Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mansooreh Mirzaie ◽  
Ahmad Barani ◽  
Naser Nematbakkhsh ◽  
Majid Mohammad-Beigi
Keyword(s):  
Probability Model ◽  
Clustering Algorithms ◽  
Large Data ◽  
Clustering Methods ◽  
Overlapping Clusters ◽  
Density Based Clustering ◽  
Real World Applications ◽  
Gene Regulatory ◽  
Large Data Analysis ◽  
Better Than

Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian density-based method (Bayesian-OverDBC) for modeling the overlapping clusters is presented. Bayesian-OverDBC can predict the formation of a new cluster. It can also predict the overlapping of cluster with existing clusters. Bayesian-OverDBC has been compared with other algorithms (nonoverlapping and overlapping models). The results show that Bayesian-OverDBC can be significantly better than other methods in analyzing microarray data.

scholarly journals Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
D. Rangaprakash ◽  
◽  
Toluwanimi Odemuyiwa ◽  
D. Narayana Dutt ◽  
Gopikrishna Deshpande
Keyword(s):  
Cognitive Impairment ◽  
Brain Connectivity ◽  
Clustering Algorithms ◽  
Training Data ◽  
Clustering Methods ◽  
Machine Learning Classification ◽  
Density Based Clustering ◽  
Supervised Classifiers ◽  
Neuroimaging Data ◽  
Robustness To Noise

AbstractVarious machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.

The Modeling and Simulation of Data Clustering Algorithms in Data Mining with Big Data

2019 ◽  
Vol 04 (01) ◽  
pp. 1850017 ◽  
Author(s):  
Weiru Chen ◽  
Jared Oliverio ◽  
Jin Ho Kim ◽  
Jiayue Shen
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Reduction ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
High Volume ◽  
Clustering Methods ◽  
Data Set ◽  
Processing Methods ◽  
Integration Data

Big Data is a popular cutting-edge technology nowadays. Techniques and algorithms are expanding in different areas including engineering, biomedical, and business. Due to the high-volume and complexity of Big Data, it is necessary to conduct data pre-processing methods when data mining. The pre-processing methods include data cleaning, data integration, data reduction, and data transformation. Data clustering is the most important step of data reduction. With data clustering, mining on the reduced data set should be more efficient yet produce quality analytical results. This paper presents the different data clustering methods and related algorithms for data mining with Big Data. Data clustering can increase the efficiency and accuracy of data mining.

scholarly journals An Efficient Density Based Clustering approach for High Dimensional Data

2018 ◽  
Vol 7 (2.32) ◽  
pp. 111
Author(s):  
Y Vijay Bhaskhar Reddy PP COMP.SCI.0560 ◽  
Dr L.S.S Reddy ◽  
Dr S.S.N. Reddy
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Data Extraction ◽  
Clustering Techniques ◽  
Density Based Clustering ◽  
Large Databases ◽  
Clustering Approach ◽  
Effective Analysis

Data extraction, data processing, pattern mining and clustering are the important features in data mining. The extraction of data and formation of interesting patterns from huge datasets can be used in prediction and decision making for further analysis. This improves, the need for efficient and effective analysis methods to make use of this data. Clustering is one important technique in data mining. In clustering a set of items are divided into several clusters where inter-cluster similarity is minimized and intra-cluster similarity is maximized. Clustering techniques are easy to identify of class in large databases. However, the application to large databases rises the following requirements for clustering techniques: minimal requirements of domain knowledge to determine the input specifications, invention of clusters with absolute shape & certainty of large databases.. The existing clustering techniques offer no solution to the combination of requirements. The proposed clustering technique DBSCAN using KNN relying on a density-based notion of clusters which is accomplished to discover clusters of arbitrary shape.  

scholarly journals Performance Comparison with Hierarchical and Partitional Clustering Methods

2021 ◽  
Vol 20 ◽  
pp. 177-184
Author(s):  
Ozer Ozdemir ◽  
Simgenur Cerman
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Performance Comparison ◽  
Hierarchical Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Partitional Clustering ◽  
Statistical Programming ◽  
Using Data

In data mining, one of the commonly-used techniques is the clustering. Clustering can be done by the different algorithms such as hierarchical, partitioning, grid, density and graph based algorithms. In this study first of all the concept of data mining explained, then giving information the aims of using data mining and the areas of using and then clustering and clustering algorithms that used in data mining are explained theoretically. Ultimately within the scope of this study, "Mall Customers" data set that taken from Kaggle database, based partitioned clustering and hierarchical clustering algorithms aimed at the separation of clusters according to their costumers features. In the clusters obtained by the partitional clustering algorithms, the similarity within the cluster is maximum and the similarity between the clusters is minimum. The hierarchical clustering algorithms is based on the gathering of similar features or vice versa. The partitional clustering algorithms used; k-means and PAM, hierarchical clustering algorithms used; AGNES and DIANA are algorithms. In this study, R statistical programming language was used in the application of algorithms. At the end of the study, the data set was run with clustering algorithms and the obtained analysis results were interpreted.

scholarly journals A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

2021 ◽  
pp. 412-418
Author(s):  
Aastha Gupta ◽  
Himanshu Sharma ◽  
Anas Akhtar
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Analytical Techniques ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
The Many ◽  
Data Elements

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

THE POSSIBILITIES OF CLUSTERING LEARNING METHODS IN STUDENT EDUCATION

2019 ◽  
Vol 5 ◽  
pp. 344
Author(s):  
Pēteris Grabusts
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Clustering Algorithms ◽  
Simulation Models ◽  
Clustering Methods ◽  
Learning Methods ◽  
Mining Methods

Many educational courses operate with models that were previously available only in mathematics or other learning disciplines. As a possible solution, there could be the use of package IBM SPSS Statistics and Modeler in realization of different algorithms for IT studies. Series of research were carried out in order to demonstrate the suitability of the IBM SPSS for the purpose of visualization of various simulation models of some data mining disciplines – particularly cluster analysis. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic and clustering. Clustering methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies, clustering algorithms and real examples are demonstrated.

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