Investigation Bat Algorithm Based on Artificial Bee Colony and C-means

In this study a Bat algorithm clustering is proposed that is based on Artificial Bee Colony (ABC)and Fuzzy C-means (FCM). When FCM is applied on high dimensional dataset, it usually resultsin local optimal partitioning. In this paper we address this problem and used a recently developedevolutionary technique named Artificial Bee Colony in combination to FCM. Hence, the name isFuzzy Cmeans Bee (FCB) algorithm. The method can detect globally optimal cluster centeroidsbetter than FCM as a most wildly used and popular clustering technique. To demonstrateperformance of the proposed algorithm of FCM we used it for some standard dataset UCI datasets.The results show that FCB converge to global faster than FCM and ABC.

Keywords:

Fuzzy C-means , Artificial Bee Colony , Optimization , Clustering technique