A new selection strategy for selective cluster ensemble based on Diversity and Independency

TL;DR

This paper proposes a novel cluster ensemble selection strategy combining Diversity and Independency metrics, utilizing a new heuristic and modeling language to improve accuracy over existing methods.

Contribution

It introduces Independency as a new metric, a heuristic for its calculation, and a modeling language (CAIL) to enhance cluster ensemble selection.

Findings

Significant accuracy improvement over existing methods

Effective use of Independency and Diversity metrics

Validated on multiple standard datasets

Abstract

This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection. Although quality can improve the final results in cluster ensemble, it cannot control the procedures of generating basic results, which causes a gap in prediction of the generated basic results' accuracy. Instead of quality, this paper introduces Independency as a supplementary method to be used in conjunction with Diversity. Therefore, this paper uses a heuristic metric, which is based on the procedure of converting code to graph in Software Testing, in order to calculate the Independency of two basic clustering algorithms. Moreover, a new modeling language, which we called as "Clustering Algorithms Independency Language" (CAIL), is introduced in order to generate graphs which depict Independency of algorithms. Also, Uniformity, which is a new similarity metric, has been introduced for evaluating the diversity of basic results. As a credential, our experimental results on varied different standard data sets show that the proposed framework improves the accuracy of final results dramatically in comparison with other cluster ensemble methods.