Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm
With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering a...
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MDPI AG
2020-09-01
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Series: | Applied Sciences |
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Online Access: | https://www.mdpi.com/2076-3417/10/18/6566 |
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author | Wenbing Chang Xinpeng Ji Yinglai Liu Yiyong Xiao Bang Chen Houxiang Liu Shenghan Zhou |
author_facet | Wenbing Chang Xinpeng Ji Yinglai Liu Yiyong Xiao Bang Chen Houxiang Liu Shenghan Zhou |
author_sort | Wenbing Chang |
collection | DOAJ |
description | With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance. |
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institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T16:11:56Z |
publishDate | 2020-09-01 |
publisher | MDPI AG |
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series | Applied Sciences |
spelling | doaj.art-c4b3161916bc4d929843516b6590a9542023-11-20T14:24:32ZengMDPI AGApplied Sciences2076-34172020-09-011018656610.3390/app10186566Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering AlgorithmWenbing Chang0Xinpeng Ji1Yinglai Liu2Yiyong Xiao3Bang Chen4Houxiang Liu5Shenghan Zhou6School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaWith the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance.https://www.mdpi.com/2076-3417/10/18/6566students’ behaviorK-MeansCFSFDPSSEdensitydistance |
spellingShingle | Wenbing Chang Xinpeng Ji Yinglai Liu Yiyong Xiao Bang Chen Houxiang Liu Shenghan Zhou Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm Applied Sciences students’ behavior K-Means CFSFDP SSE density distance |
title | Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm |
title_full | Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm |
title_fullStr | Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm |
title_full_unstemmed | Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm |
title_short | Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm |
title_sort | analysis of university students behavior based on a fusion k means clustering algorithm |
topic | students’ behavior K-Means CFSFDP SSE density distance |
url | https://www.mdpi.com/2076-3417/10/18/6566 |
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