An extensive comparative study of cluster validity indices

The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices.

► We compare 30 cluster validity indices (CVIs) in 720 synthetic and 20 real datasets.
► We use a new comparison methodology and three clustering algorithms: k-means, Ward and Average-linkage.
► The CVI performance drops dramatically when noise is present or clusters overlap.
► Statistical tests suggest a division of three groups of CVIs.