A new nonparametric interpoint distance-based measure for assessment of clustering

Abstract

A new interpoint distance-based measure is proposed to identify the optimal number of clusters present in a data set. Designed in nonparametric approach, it is independent of the distribution of given data. Interpoint distances between the data members make our cluster validity index applicable to univariate and multivariate data measured on arbitrary scales, or having observations in any dimensional space where the number of study variables can be even larger than the sample size. Our proposed criterion is compatible with any clustering algorithm, and can be used to determine the unknown number of clusters or to assess the quality of the resulting clusters for a data set. Demonstration through synthetic and real-life data establishes its superiority over the well-known clustering accuracy measures of the literature.

30 pages, 3 figures

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Cited by in corpus (2)

• A new measure for assessment of clustering based on kernel density estimation
• Clustering of eclipsing binary light curves through functional principal component analysis