Clustering-Based Artificial Rabbit Optimization: Approach for Enhanced Data Clustering
Balqees Ayal-Awwad, Jamil Al‐Sawwa, Mohammad Almseidin, Maen Marwan Alzubi · 2025
Data clustering is a data mining task used to group data points based on their similarity. The goal of clustering is to divide$N$data points into$K$clusters while minimizing the intra-cluster distance and maximizing the inter-cluster distance. Data clustering can be viewed as a combinatorial optimization problem, which is known as NP-hard. Thus, nature-inspired optimization algorithms have been applied to find better solutions for clustering problems. In this paper, the clustering-based artificial rabbit optimization (CARO) method is proposed. The main idea is to find the optimal centroids for the$K$clusters by minimizing the sum of squared errors. Using seven benchmark datasets, the experimental results showed that the CARO achieved outstanding results compared to six nature-inspired optimization algorithms in terms of purity and entropy.