A modified ant-based text clustering algorithm with semantic similarity measure

Journal of Systems Science and Systems Engineering

Volumn 15, Issue 4, 2006, Pages 474-492

  Xia, Haoxiang ^a   Wang, Shuguang ^b   Yoshida, Taketoshi ^c  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b BHR Frontline Technologies (Dalian) Corporation Ltd   (China)

^c JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

Ant movement rule; Ant based clustering; Semantic similarity measure; Text clustering

Indexed keywords

EID: 33845629466     PISSN: 10043756     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11518-006-5029-z     Document Type: Article

References (38)

1. Ankerst, M., Breunig, M., Kriegel, H.P. & Sander, J. (1999). OPTICS: Ordering points to identify clustering structure. In: Proceedings of the ACM SIGMOD Conference, pp. 49-60
2. Baeza-Yates, R. & Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison-Wesley, Boston, MA, USA
3. Beni, G. & Wang, U. (1989). Swarm intelligence in cellular robotic systems. In: Proceedings of NATO Advanced Workshop on Robots and Biological Systems. Tuscany, Italy, 1989
4. Berry, M. (ed.) (2003). Survey of Text Mining: Clustering, Classification, and Retrieval. Springer, New York
5. Beyer, K. Goldstein, J. Ramakrishnan, R. & Shaft, U. (1999). When is "nearest neighbor" meaningful? In: Proceedings of 7th International Conference of Database Theory (ICDT99), LNCS 1540, pp. 217-235. Springer, Berlin
6. Bonabeau, E., Dorigo, M. & Theraulaz, G (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York
7. Chen, L., Xu, X. & Chen, Y. (2004). An adaptive ant colony clustering algorithm. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics (ICMLC04), pp.1387-1392
8. Chiou, Y-C. & Lan, L.W. (2000). Genetic clustering algorithms. European Journal of Operational Research, 135: 413-427
9. Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C. & Chétien, L. (1991). The dynamics of collective sorting: robot-like ants and ant-like robots, In: Proceedings of the 1st International Conference on Simulation of Adaptive Behaviour, pp. 356-363, MIT Press, Cambridge, MA
10. Handl, J., Knowles, J. & Dorigo, M. (2003). On the performance of ant-based clustering. In: Proceedings of the Third International Conference on Hybrid Intelligent Systems, pp. 204-213, IOS Press
11. Handl, J. & Meyer, B. (2002). Improved ant-based clustering and sorting in a document retrieval interface. In: Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature (PPSN VII), pp. 913-923, Springer-Verlag, Berlin
12. Hartigan, J. & Wong, M. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 28:100-108
13. Hoe, K., Lai, W. & Tai, T. (2002). Homogeneous ants for Web document similarity modeling and categorization. In: Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature, pp. 256-261
14. Hotho, A., Staab, S. & Stumme, G. (2003). Wordnet improves text document clustering. In: Proceedings of the Semantic Web Workshop at SIGIR-2003, 26th Annual International ACM SIGIR Conference, Toronto, Canada. July 28-August 1, 2003
15. Jain, A.K., Murty, M.N. & Flynn, P.J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3): 264-323
16. Jing, L., Zhou, L., Ng, M. K. & Huang, J.Z. (2006). Ontology-based distance measure for text clustering. In: Proceedings of the 4th Workshop on Text Mining, 6th SIAM International Conference on Data Mining
17. Kanade, P. & Hall, L.O. (2003). Fuzzy ants as a clustering concept. In: Proceedings of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232
18. Kuntz, P., Snyers, D. & Layzell, P. (1998). A stochastic heuristic for visualising graph clusters in a bi-dimensional space prior to partitioning. Journal of Heuristics, 5: 327-351
19. Labroché, N. Monmarché, N. & Venturini, G (2002). A new clustering algorithm based on the chemical recognition system of ants. In: Proceedings of the 2002 European Conference on Artificial Intelligence, pp. 345-349
20. Lewis, D. (2006). Reuters-21578 text categorization test collection. Available via: http://www.daviddlewis.com/resources/testcollections/ reuters21578. Cited Nov. 10, 2006.
21. Li, Y., Bandar, Z. & McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 13: 871-882
22. Lin, D. (1998). An information-theoretic definition of similarity. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp.296-304
23. Lindberg, D. A., Siegel, E. R., Rapp, B. A., Wallingford, K. T. & Wilson, S. R. (1993). Use of MEDLINE by physicians for clinical problem solving. Journal of the American Medical Association, 269: 3124-3129
24. Lumer, E. & Faieta, B. (1994). Diversity and adaption in populations of clustering ants. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour, MIT Press, Cambridge, MA
25. Megaputer Intelligence Inc. (2006). Online introduction to TextAnalyst™. Available via: http://www.megaputer.com/products/, Cited Nov. 12, 2006
26. Miller, G (1995). Wordnet: a lexical database for English. Communications of the ACM, 38: 39-41
27. Monmarché, N. (1999). On data clustering with artificial ants. In: AAAI-99 and GECCO-99 Workshop on Data Mining with Evolutionary Algorithms: Research Directions, pp.23-26
28. Rada, R., Mili, H., Bicknell, E. & Bletiner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19:17-30
29. Ramos, V. & Abraham, A. (2005). ANTIDS: self organized ant based clustering model for intrusion detection system. In: Proceedings of The Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST'05), pp. 977-986, Springer-Verlag, Berlin
30. Ramos, V. & Merelo, J. (2002). Self-organized stigmergic document maps: environments as a mechanism for context learning. In: Proceedings of the First Spanish Conference on Evolutionary and Bio-Inspired Algorithms, pp. 284-293
31. Rector, A., Gangemi, A., Galeazzi, E., Glowinski. A. & Rossi-Mori A. (1994). The GALEN CORE model schemata for anatomy: towards a reusable application-independent model of medical concepts. In: Proceedings of Medical Informatics Europe (MIE94)
32. Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of 14th International Joint Conference on Artificial Intelligence
33. Salton, G., Fox, E. & Wu, H. (1983). Extended boolean information retrieval. Communications of the ACM, 26: 1022-1036
34. Salton, G., Wong, A. & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11): 613-620
35. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34: 1-47.
36. Vizine, A.L., de Castro, L.N., Hruschkal, E.R. & Gudwin, R.R. (2005). Towards improving clustering ants: an adaptive ant clustering algorithm. Informatica, 29: 143-154
37. Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58: 236-244
38. th International Symposium on Knowledge and Systems Sciences, pp. 159-166, Global-Link Publisher, Hong Kong