A Split-Merge Framework for Comparing Clusterings

TL;DR

This paper introduces a novel split-merge framework for comparing clusterings that addresses normalization issues and leverages data point information, providing more consistent and informative evaluation measures.

Contribution

It proposes a general component-based decomposition formula and a split-merge framework that improves clustering comparison by ensuring normalization and utilizing data features.

Findings

Framework outperforms existing measures in empirical tests.

Conditional normalization improves comparison consistency.

Utilizes feature vectors and pairwise distances effectively.

Abstract

Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation between two clusterings as a bipartite graph and propose a general component-based decomposition formula based on the components of the graph. Most existing measures are examples of this formula. In order to satisfy consistency in the component, we further propose a split-merge framework for comparing clusterings of different data sets. Our framework gives measures that are conditionally normalized, and it can make use of data point information, such as feature vectors and pairwise distances. We use an entropy-based instance of the framework and a coreference resolution data set to demonstrate empirically the utility of our framework over other measures.