关键词: 聚类分析, 数据挖掘, 弹性网络, 加权, 统计力学

Abstract:

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