Clustering based on improved bee colony algorithm

  • Authors:

  • Yonghao Xiao;Weiyu Yu;Yunfei Cao;Haishu Tan

  • Affiliations:

  • School of Electronic and Information Engineering, Foshan University, Foshan 528000, China/ South China University of Technology, Guangzhou 510641, China;School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China/ Provincial Key Laboratory for Computer Information Processing Technology, Soochow U ...;School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China;School of Electronic and Information Engineering, Foshan University, Foshan 528000, China

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2013

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Abstract

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'.