Document Clustering Using Hybrid Ant Algorithm
Abstract
In recent years it is required to store/retrieve a huge quantum of documents across network in World Wide Web (WWW) due to the wide spread usage of computers across the globe. This has placed many challenges to the Information Retrieval (IR) system like fetching of relevant documents matching with user’s query, classification of electronic documents etc. Clustering is an unsupervised learning that partitions the available documents into several clusters based on the similarity between the documents. The problem of clustering has become a combinatorial optimization problem in IR system due to the exponential growth in information over WWW. In this paper, a novel Hybrid Ant Algorithm, a blended scheme of Tabu Search and Ant Colony Optimatization algorithm has been proposed to form better quality clusters with documents of similar features. The viability of the proposed algorithm is tested over ma few standard benchmark datasets and the numerical experimental results reveal that the proposed algorithm yields promising quality clusters compared to other ones produced by K-means algorithm.
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J. A. Bland, “Optimal structural design by ant colony optimization,” Engineering Optimization, vol. 33, pp. 425 – 443, 2001.
E. Bonabeau, M. Dorigo and G. Theraulaz, “Swarm intelligence: From Nature to Artificial Systems,” New York: Oxford University Press, 1999
M. H. Botee and E. Bonabeau, “Evolving ant colony optimization,” Adv.Complex Systems, vol. 1, pp. 149-159, 1998
A. Colorni, M. Dorigo and V. Maniezzo, “An investigation of some properties of an ant algorithm,” in 1992 Proc. Parallel Problem Solving from Nature, Amsterdam, Elsevier, pp. 509-520
D. Costa and A. Hertz, “Ants can colour graphs,” Journal of Operational Research Society, vol. 48, pp. 295-305, 1997
M. Dorigo, “Ant algorithms solve difficult optimization problems,” in 2001 Proc. Advances in Artificial Life: Artificial Life Conf., Springer Verlag, pp. 11-22
M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comp., vol. 1, pp. 53-66, 1997
M. Dorigo and T. Stuzzle, “An experimental study of the simple ACL algorithm,” in 2001 Proc. WSES Evolutionary Computation Conf.,WSES-Press International, pp. 253-258
M. Dorigo and T. Stuzzle, “Ant colony optimization”, England: MITPress, 2004
M. Dorigo, G. Di Caro and L. M. Gambardella, “Ant algorithms for discrete optimization,” Artificial Life, vol. 5, pp. 137 – 172, 1999
M. Dorigo, V. Maniezzo and A. Colorni, “The ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics-Part-B, vol. 26, no.1, pp. 1-13, 1996
J. Holland, “Concerning efficient adaptive systems,” Self – organizing systems, Washington, D. C.: Spartan Books, pp. 215-230, 1962
J. Holland, “Adaptation in natural and artificial systems”, Ann Arbor: University of Michigan Press, 1975
Yulan He, Siu Cheung Hui, and Yongxiang Sim, “A novel ant based clustering algorithm for document clustering,” Asia Information Retrieval Symposium, pp. 537 – 544, 2006
Lukasz Machnik, “ACO based document clustering method,” Technical report , Annales UMCS Informatica AI 3, pp 315-323, 2005
J. L. Deneubourg, S. Gross, N. Franks, A. Sendova, C. Detrain and L. Chretien, “The dynamics of collective sorting: robot like ants and ant like robots,” in 1991 Proc. First International Conference on Simulation of Adaptive Behavior: From Animals to Animats, MIT Press: Cambridge,MA, pp. 356-363
E. D. Lumer and B. Faieta, “Diversity and adaptation of populations of clustering ants,” in 1994 Proc. Simulation of Adaptive Behavior Conf.,pp. 501-508
N. Monmarche, M. Silmane and G. Venturini, “On improving clustering in numerical databases with artificial ants,” Advances in Artificial Life,pp. 626-635, 1999
N. Monmarche, “On data clustering with artificial ants,” “Data mining with evolutionary algorithms: research directions”, AAAI Workshop,AAAI Press, pp. 23-26, 2005
M. Dorigo, E. Bonabeau and G. Theraulaz, “Ant algorithms and stigmergy,” Future Generation Computer Systems, vol. 16, no. 8, pp.851-871, 2000
M. Dorigo, G. Di Caro and L. M. Gambarella, “Ant algorithms for discrete optimization,” Artificial Life, vol. 5, no. 3, 137-172, 1999
V. Ramos and J. J. Merelo, “Self organized stigmergic document maps: environment as mechanism for context learning,” in 2002 Proc.Evolutionary and Bio-inspired Algorithms Conf., pp. 284-293
P. Kanade and L. O. Hall, “Fuzzy ants as a clustering concepts”, in 2003 Proc. North American Fuzzy Information Processing Society Conf., pp. 227-232.
H. Azzag, N. Monmarche, M. Slimane and G. Venturini, “Ant Tree: a new model for clustering with artificial ants,” Evolutionary Computation, vol.4, pp. 2642-2647, 2003
P. S. Shelokar, V. K. Jayaraman and B. D. Kulkarni, “An ant colony algorithm for clustering,”Analytica Chemica Acta, vol.509, no. 2, pp.187-195, 2004.
S. Chi and C. C. Yang, “Integration of ant colony SOM and K-means for clustering analysis,” Knowledge based Intelligent Information and Engineering Systems, LNCS, Springer, vol. 4251, pp. 1-8, 2006
Yan Yang and Mohamed S. Kamel, “An aggregated clustering approach using multi-ant colonies algorithms,” Pattern Recognition, vol. 39, no. 7,pp. 665-671, 2006
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