Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 43, Issue 3, Pages 982-994Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2012.2220543
Keywords
Clustering validation index based on nearest neighbors (CVNN); internal clustering validation measure; k-nearest neighbor (kNN)
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Funding
- National Science Foundation [CCF-1018151, IIP-1069258]
- National Natural Science Foundation of China [70890082, 71028002, 71271027, 70901002, 71171007, 71031001, 70890080]
- Fundamental Research Funds for the Central Universities of China [FRF-TP-10-006B]
- Foundation for the Author of National Excellent Doctoral Dissertation of China [201189]
- Program for New Century Excellent Talents in University
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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.
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