Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

TL;DR

This paper evaluates the performance of the evolutionary clustering algorithm star (ECA*) against traditional clustering methods on heterogeneous datasets, highlighting its strengths in cluster number detection and robustness to dataset features.

Contribution

It introduces a comprehensive performance rating framework and demonstrates ECA*'s advantages over other algorithms in specific clustering tasks.

Findings

ECA* outperforms other algorithms in determining the correct number of clusters.

ECA* shows less sensitivity to dataset feature variations.

Limitations include lack of prior knowledge assumptions and real-world application testing.

Abstract

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation (EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.