A Self-Learning Clustering Algorithm Based on Clustering Coefficient

被引：0

作者：

Zhong, Mingjie ^{[1
]}

Ding, Zhijun ^{[1
]}

Sun, Haichun ^{[1
]}

Wang, Pengwei ^{[2
]}

机构：

[1] The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai

[2] Department of Computer Science, University of Pisa, Pisa

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8786卷

基金：

中国国家自然科学基金;

关键词：

Clustering algorithm; Clustering coefficient; Self-learning clustering;

D O I：

10.1007/978-3-319-11749-2_6

中图分类号：

学科分类号：

摘要：

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e.g. hotspots discovery. © Springer International Publishing Switzerland 2014.

引用

页码：79 / 94

页数：15

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