Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (22): 55-65.DOI: 10.3778/j.issn.1002-8331.2001-0215

Previous Articles     Next Articles

Research on Clustering Algorithm of Elastic Net with Weighted Characteristics

YI Junyan, WU Boya, YONG Qiaoling   

  1. 1.School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    2.School of Computer Science and Technology, Kashgar University, Kashgar, Xinjiang 844000, China
  • Online:2020-11-15 Published:2020-11-13



  1. 1.北京建筑大学 电气与信息工程学院,北京 100044
    2.喀什大学 计算机科学与技术学院,新疆 喀什 844000


Clustering analysis is one of the important contents in data mining. As a tool, clustering is important for people to analyze data. The weighted clustering direction of the elastic net is studied for the problems in clustering which include noise, high-dimensional and so on. This algorithm takes into account the importance of each feature in data set for the clustering process, reconstructs the energy function of the associated data points and cluster center points. Combining the maximum entropy principle, the idea of simulated annealing and the solution mode of elastic net method, a Weighting of Elastic Net for Clustering(WENC) algorithm is proposed. This algorithm can achieve high quality clustering results without manual training. Experimental results on both synthetic data sets and UCI real data sets show that WENC algorithm improves the quality of clustering.

Key words: clustering analysis, data mining, elastic net, weighting, statistical mechanics



关键词: 聚类分析, 数据挖掘, 弹性网络, 加权, 统计力学