Artificial Bee Colony Algorithm Integrated with Fuzzy C-mean Operator for Data Clustering
- 1 , India
Copyright: © 2020 M. Krishnamoorthi and A. M. Natarajan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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 C- Means (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.
- ABC Algorithm
- FCM Algorithm
- FCM Operator