Understanding of Internal Clustering Validation Measures

TLDR

Clustering validation is essential for successful clustering, and it is generally divided into external and internal measures. The study focuses on internal clustering validation, examining 11 widely used measures for crisp clustering. The authors analyze these 11 measures across five conventional clustering aspects to assess their validation properties. Experiments reveal that S_Dbw outperforms the other measures across all five aspects, whereas the remaining measures exhibit limitations in specific scenarios.

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 detailed study of 11 widely used internal clustering validation measures for crisp clustering. From five conventional aspects of clustering, we investigate their validation properties. Experiment results show that S_Dbw is the only internal validation measure which performs well in all five aspects, while other measures have certain limitations in different application scenarios.