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'.

[1]  Khaled S. Al-Sultan,et al.  A Tabu search approach to the clustering problem , 1995, Pattern Recognit..

[2]  P. Balasubramanie,et al.  Multi-document summarisation using genetic algorithm-based sentence extraction , 2011, Int. J. Comput. Appl. Technol..

[3]  T. Seeley.  The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies , 1995 .

[4]  T. Seeley,et al.  Collective decision-making in honey bees: how colonies choose among nectar sources , 1991, Behavioral Ecology and Sociobiology.

[5]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[6]  G. Flake.  The Computational Beauty of Nature , 1998 .

[7]  K. Frisch.  Bees: their vision, chemical senses, and language , 1950 .

[8]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining , 2009, Swarm Intelligence in Data Mining.

[9]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[10]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[11]  Lydia Boudjeloud-Assala,et al.  Visual interactive evolutionary algorithm for high dimensional outlier detection and data clustering problems , 2012 .

[12]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[13]  Lifeng Xi,et al.  A heuristics method based on ant colony optimisation for redundancy allocation problems , 2011, Int. J. Comput. Appl. Technol..

[14]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[15]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[16]  M. Rockstein.  Bees. Their Vision, Chemical Senses, and Language , 1952 .

[17]  Sameh Otri,et al.  Data clustering using the bees algorithm , 2007 .

[18]  Hichem Frigui,et al.  A Robust Competitive Clustering Algorithm With Applications in Computer Vision , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  C. A. Murthy,et al.  In search of optimal clusters using genetic algorithms , 1996, Pattern Recognit. Lett..

[20]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[21]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .

[22]  Andreas Rudolph,et al.  Techniques of Cluster Algorithms in Data Mining , 2002, Data Mining and Knowledge Discovery.

[23]  S. Camazine,et al.  A model of collective nectar source selection by honey bees , 1991 .

[24]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[25]  Yong Yue,et al.  Genetic algorithm based solution to dead-end problems in robot navigation , 2011, Int. J. Comput. Appl. Technol..

[26]  Ali Maroosi,et al.  Application of honey-bee mating optimization algorithm on clustering , 2007, Appl. Math. Comput..

[27]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[28]  Bijaya K. Panigrahi,et al.  Modified biogeography-based optimisation (MBBO) , 2011, Int. J. Bio Inspired Comput..

[29]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.