A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets

TL;DR

This paper introduces ECAStar, a novel evolutionary clustering algorithm that combines meta-heuristics, statistical techniques, and traditional methods to generate semantically meaningful clusters in heterogeneous datasets.

Contribution

The paper presents a new evolutionary clustering algorithm, ECAStar, integrating social class ranking, Levy flight, and statistical methods for analyzing complex datasets.

Findings

ECAStar outperforms five conventional clustering algorithms in experiments.

ECAStar produces more meaningful and semantically rich clusters.

The approach effectively handles heterogeneous and multi-featured datasets.

Abstract

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multi-disciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECAStar against five conventional approaches: K-means (KM), K-meansPlusPlus (KMPlusPlus), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clusteringPlusPlus (GENCLUSTPlusPlus).