Data Clustering using Metaheuristic Algorithms

A clustering problem can be formulated mathematically as an optimization problem. In this work, a metaheuristic algorithm called the water cycle algorithm, based on the evaporation rate is used in conjunction with a local search method namely Hookes and Jeeves method to perform data clustering.

Figures: Convergence performance of the proposed HJ-WCAER against the original WCAER method on the Iris dataset, the Glass dataset and Wisconsin Breast Cancer dataset using the Euclidean distance and Davies-Bouldin Index.

Related article:

Hasnanizan Taib and Ardeshir Bahreininejad. Data clustering using hybrid water cycle algorithm and a local pattern search method. Advances in Engineering Software, 153:102961, 2021. https://doi.org/10.1016/j.advengsoft.2020.102961.

Hasnanizan Taib and Ardeshir Bahreininejad. Enhanced water cycle algorithm using Hookes and Jeeves method for clustering large gas data. AIP Conference Proceedings, 2643, 050056 (2023). https://doi.org/10.1063/5.0110484.