ESDF: Ensemble Selection using Diversity and Frequency

TL;DR

This paper introduces an efficient ensemble selection method for consensus clustering that prioritizes partitions based on diversity and frequency, improving quality and reducing computational costs.

Contribution

It proposes a novel ensemble selection approach using diversity and frequency metrics, outperforming previous methods in consensus clustering tasks.

Findings

Better consensus quality than existing methods

Reduces computational overhead by selecting fewer partitions

Effective across multiple datasets

Abstract

Recently ensemble selection for consensus clustering has emerged as a research problem in Machine Intelligence. Normally consensus clustering algorithms take into account the entire ensemble of clustering, where there is a tendency of generating a very large size ensemble before computing its consensus. One can avoid considering the entire ensemble and can judiciously select few partitions in the ensemble without compromising on the quality of the consensus. This may result in an efficient consensus computation technique and may save unnecessary computational overheads. The ensemble selection problem addresses this issue of consensus clustering. In this paper, we propose an efficient method of ensemble selection for a large ensemble. We prioritize the partitions in the ensemble based on diversity and frequency. Our method selects top K of the partitions in order of priority, where K is decided by the user. We observe that considering jointly the diversity and frequency helps in identifying few representative partitions whose consensus is qualitatively better than the consensus of the entire ensemble. Experimental analysis on a large number of datasets shows our method gives better results than earlier ensemble selection methods.