MOCICE-BCubed F$_1$: A New Evaluation Measure for Biclustering Algorithms

TL;DR

This paper introduces MOCICE-BCubed F$_1$, a new evaluation measure for biclustering algorithms that effectively handles overlaps and satisfies comprehensive meta-evaluation conditions.

Contribution

The paper presents a novel external measure for biclustering evaluation that meets extensive meta-evaluation criteria and extends to traditional clustering scenarios.

Findings

Handles overlapping in object and feature spaces

Satisfies comprehensive meta-evaluation conditions

Effective for both biclustering and traditional clustering

Abstract

The validation of biclustering algorithms remains a challenging task, even though a number of measures have been proposed for evaluating the quality of these algorithms. Although no criterion is universally accepted as the overall best, a number of meta-evaluation conditions to be satisfied by biclustering algorithms have been enunciated. In this work, we present MOCICE-BCubed F$_1$, a new external measure for evaluating biclusterings, in the scenario where gold standard annotations are available for both the object clusters and the associated feature subspaces. Our proposal relies on the so-called micro-objects transformation and satisfies the most comprehensive set of meta-evaluation conditions so far enunciated for biclusterings. Additionally, the proposed measure adequately handles the occurrence of overlapping in both the object and feature spaces. Moreover, when used for evaluating traditional clusterings, which are viewed as a particular case of biclustering, the proposed measure also satisfies the most comprehensive set of meta-evaluation conditions so far enunciated for this task.