Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Abstract

Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses a Fuzzy CMeans (FCM) operator in the Artificial Bee Colony (ABC) algorithm. The area of action of the FCM operator comes at the scout bee phase of the ABC algorithm as the scout bees are introduced by the FCM operator. The experimental results have shown that the proposed approach has provided significant results in terms of the quality of solution. The comparative study of the proposed approach with existing algorithms in the literature using the datasets from UCI Machine learning repository is satisfactory.

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

“…Additionally, an overlapping area concept has been proposed for the purpose of information sharing among different subpopulations. Krishnamoorthi and Natarajan (2013) proposed an optimization method based on the ABC algorithm for the purpose of clustering. It uses an FCM operator for the scout bees.…”

Fuzzy C Means (FCM) Clustering Based Hybrid Swarm Intelligence Algorithm for Test Case Optimization

“…This study uses a couple of recent swarm intelligence methods called as the Particle Swarm Algorithm and Artificial Bee Colony integrated with Fuzzy C-Means (ABC-FCM) Algorithm for Test Case Optimization (Krishnamoorthi and Natarajan, 2013). The research reinstates that the optimized suite produced by PSABC covers the faults entirely in a program when compared with other approaches.…”