Reviews on Determining the Number of Clusters

Authors

Shuo Xu, Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Rd., Haidian District, Beijing 100038, P.R. ChinaFollow
Xiaodong Qiao, Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Rd., Haidian District, Beijing 100038, P.R. ChinaFollow
Lijun Zhu, Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Rd., Haidian District, Beijing 100038, P.R. ChinaFollow
Yunliang Zhang, Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Rd., Haidian District, Beijing 100038, P.R. ChinaFollow
Chunxiang Xue, Department of Information Management, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Xuanwu District, Nanjing 210094, P.R. ChinaFollow
Lin Li, College of Information and Electrical Engineering, China Agricultural University (East Campus), No. 17 Qinghua East Rd., Haidian District, Beijing 100083, P.R. ChinaFollow

Author Country (or Countries)

P.R. China

Abstract

Clustering analysis seeks to partition a given dataset into groups or clusters so that the data objects within a cluster are more similar to each other than the objects in different clusters. A very rich literature on clustering analysis has developed over the past three decades. But a crucial question still remains unanswered: how many clusters are contained in the population on earth when only an observed set of samples is available? The goal of this paper is to provide a comprehensive review of approaches on determining the ”correct” number of clusters. In particular, we divide these approaches into three categories: internal measures, external measures, and clustering stability based methods. Then, we introduce several representative examples, and present specific challenges pertinent to each category. Finally, the promising trends are suggested in this field.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/100428

Recommended Citation

Xu, Shuo; Qiao, Xiaodong; Zhu, Lijun; Zhang, Yunliang; Xue, Chunxiang; and Li, Lin (2016) "Reviews on Determining the Number of Clusters," Applied Mathematics & Information Sciences: Vol. 10: Iss. 4, Article 28.
DOI: http://dx.doi.org/10.18576/amis/100428
Available at: https://digitalcommons.aaru.edu.jo/amis/vol10/iss4/28