A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
This work is an incremental improvement for researchers and practitioners working with heterogeneous datasets, aiming to produce more semantically meaningful clusters.
The paper proposes ECAStar, a new evolutionary clustering algorithm, to address the challenge of clustering heterogeneous and multi-featured datasets. It aims to generate more semantically meaningful clusters compared to existing deterministic methods. The algorithm integrates evolutionary operators, Levy flight optimization, statistical techniques like quartiles and percentiles, and Euclidean distance from K-means.
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).