title: Understanding and enhancement of internal clustering validation measures.
abstract: Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a study of 11 widely used internal clustering validation measures for crisp clustering. The results of this study indicate that these existing measures have certain limitations in different application scenarios. As an alternative choice, we propose a new internal clustering validation measure, named clustering validation index based on nearest neighbors (CVNN), which is based on the notion of nearest neighbors. This measure can dynamically select multiple objects as representatives for different clusters in different situations. Experimental results show that CVNN outperforms the existing measures on both synthetic data and real-world data in different application scenarios.
authors.affiliation: Department of Information Systems, New Jersey Institute of Technology, Newark, NJ 07102, USA. yl473@njit.edu
authors.forename: Yanchi
authors.initials: Y
authors.lastname: Liu
authors.forename: Zhongmou
authors.initials: Z
authors.lastname: Li
authors.forename: Hui
authors.initials: H
authors.lastname: Xiong
authors.forename: Xuedong
authors.initials: X
authors.lastname: Gao
authors.forename: Junjie
authors.initials: J
authors.lastname: Wu
authors.forename: Sen
authors.initials: S
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*