Document Clustering Evaluation: Divergence from a Random Baseline

TL;DR

This paper introduces a divergence from a random baseline technique for evaluating document clustering quality, which normalizes measures and distinguishes effective from ineffective clusterings across various evaluation methods.

Contribution

It presents a novel evaluation approach using divergence from a random baseline applicable to any cluster quality measure, enhancing the assessment of clustering effectiveness.

Findings

Differentiates ineffective clusterings in XML Mining

Normalizes cluster quality measures similar to NMI

Provides clear optima in distortion measures like RMSE

Abstract

Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality. This includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval. The divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the INEX XML Mining track. It also appears to perform a normalisation similar to the Normalised Mutual Information (NMI) measure but it can be applied to any measure of cluster quality. When it is applied to the intrinsic measure of distortion as measured by RMSE, subtraction from a random baseline provides a clear optimum that is not apparent otherwise. This approach can be applied to any clustering evaluation. This paper describes its use in the context of document clustering evaluation.