A new validity index for evaluating the clustering results by partitional clustering algorithms

Shihong Yue, Jianpei Wang, Jeen-Shing Wang, Xiujuan Bao

研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)

摘要

Partitional clustering algorithms are the most widely used approach in clustering problems. However, how to evaluate the clustering performance of these algorithms remains unanswered due to the lack of an efficient measure for accurately representing the separation among partitioned clusters. In this paper, based on two most commonly used partitional clustering algorithms, c-means and fuzzy c-means, and their variants, we developed a new measure, called as dual center, to represent the separation among clusters. The new measure can efficiently represent the separation among various clusters. According to the defined measure, a new validity index is proposed for evaluating the clustering performance of partitional algorithms. Two groups of benchmark datasets with different characteristics were used to validate the effectiveness of the proposed validity index. Experimental results provide evidence that the proposed validity index outperforms some existing representative validity indexes in the two groups of typical and representative datasets.

原文English
頁(從 - 到)1127-1138
頁數12
期刊Soft Computing
20
發行號3
DOIs
出版狀態Published - 2016 三月 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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