Clustering based on improved bee colony algorithm

Clustering is concerned with partitioning a dataset into homogeneous groups. One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. K-means clustering involves search and optimisation. The main problem with this clustering method is its tendency to converge to local optima. Bee colony algorithm has emerged as one of the robust and efficient global search heuristics of current interest. This paper describes an application of improved bee colony algorithm to the clustering of data and image segmentation. In contrast to most of the existing clustering techniques, the proposed approach requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data 'on the run'.