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...
Main Authors: | Wenbing Chang, Xinpeng Ji, Yinglai Liu, Yiyong Xiao, Bang Chen, Houxiang Liu, Shenghan Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/18/6566 |
Similar Items
-
A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering
by: Wenbing Chang, et al.
Published: (2018-12-01) -
Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree
by: Song Chen, et al.
Published: (2023-01-01) -
The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification
by: Andi Besse Firdausiah Mansur, et al.
Published: (2018-10-01) -
An Energy-Balanced Clustering Protocol Based on an Improved CFSFDP Algorithm for Wireless Sensor Networks
by: Yiming Zhang, et al.
Published: (2018-03-01) -
Energy Efficient Distance Computing: Application to K-Means Clustering
by: Yong Shim, et al.
Published: (2022-01-01)