On the Use of Relative Validity Indices for Comparing Clustering Approaches

TL;DR

This paper critically examines the use of Relative Validity Indices for selecting similarity paradigms in clustering, revealing fundamental limitations and recommending alternative validation methods based on external criteria and domain knowledge.

Contribution

It provides the first comprehensive empirical and theoretical analysis of RVIs for similarity paradigm selection, highlighting their unsuitability and proposing more rigorous validation approaches.

Findings

RVIs are unreliable for similarity paradigm selection.

Fundamental conceptual limitations undermine RVI use.

External validation is recommended for better SP selection.

Abstract

Relative Validity Indices (RVIs) such as the Silhouette Width Criterion and Davies Bouldin indices are the most widely used tools for evaluating and optimising clustering outcomes. Traditionally, their ability to rank collections of candidate dataset partitions has been used to guide the selection of the number of clusters, and to compare partitions from different clustering algorithms. However, there is a growing trend in the literature to use RVIs when selecting a Similarity Paradigm (SP) for clustering - the combination of normalisation procedure, representation method, and distance measure which affects the computation of object dissimilarities used in clustering. Despite the growing prevalence of this practice, there has been no empirical or theoretical investigation into the suitability of RVIs for this purpose. Moreover, since RVIs are computed using object dissimilarities, it remains unclear how they would need to be implemented for fair comparisons of different SPs. This study presents the first comprehensive investigation into the reliability of RVIs for SP selection. We conducted extensive experiments with seven popular RVIs on over 2.7 million clustering partitions of synthetic and real-world datasets, encompassing feature-vector and time-series data. We identified fundamental conceptual limitations undermining the use of RVIs for SP selection, and our empirical findings confirmed this predicted unsuitability. Among our recommendations, we suggest instead that practitioners select SPs by using external validation on high quality labelled datasets or carefully designed outcome-oriented objective criteria, both of which should be informed by careful consideration of dataset characteristics, and domain requirements. Our findings have important implications for clustering methodology and evaluation, suggesting the need for more rigorous approaches to SP selection.