A new clustering method based on the bio‐inspired cuttlefish optimization algorithm. Issue 2 (4th December 2019)

• Record Type: Journal Article
• Authors: Eesa, Adel Sabry; Orman, Zeynep
• Is Part Of: Expert systems. Volume 37:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-04
• Subjects: clustering -- cuttlefish optimization algorithm -- genetic algorithm -- K‐means algorithm -- particle swarm optimization; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12478
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Most of the well‐known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio‐inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K‐means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA‐Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.