Fully Controllable Ant Colony System for Text Data Clustering

1. Deneubourg, J.L., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behavior 3(159) (1990)

   Google Scholar 

2. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of ECAL 1991: European Conference on Artificial Life, pp. 134–142. Elsevier, Paris (1991)

   Google Scholar 

3. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

   Article  Google Scholar 

4. Cheng, C.-B., Mao, C.-P.: A modified ant colony system for solving the travelling salesman problem with time windows. Mathematical and Computer Modelling (46), 1225–1235 (2007)

   Article  MathSciNet  MATH  Google Scholar 

5. Gambardella, L.M.: Ant-Q: A reinforcement learning approach to the traveling salesman problem. In: International Conference on Machine Learning, pp. 252–260 (1995)

   Google Scholar 

6. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B 26(1), 29–41 (1996)

   Article  Google Scholar 

7. Salman, A., Ahmad, I., Al-Madani, S.: Particle swarm optimization for task assignment problem. Microprocessors and Microsystems 26, 363–371 (2002)

   Article  Google Scholar 

8. Socha, K., Knowles, J., Sampels, M.: A Max-Min ant system for the university course timetabling problem. IRIDIA, Université Libre de Bruxelles, CP 194/6 (2003)

   Google Scholar 

9. Socha, K.: A Max-Min ant system for international timetabling competition. IRIDIA, Université Libre de Bruxelles, CP 194/6 (March)

   Google Scholar 

10. Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats, vol. 3 (1994)

    Google Scholar 

11. Bin, W., Yi, Z., Shaohui, L., Zhongzhi, S.: CSIM: A document clustering algorithm based on swarm intelligence. In: Evolutionary Computation, CEC 2002, vol. (1), pp. 477–482 (2002)

    Google Scholar 

12. Salton, G.: The smart retrieval system. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

13. Salton, G., McG’ill, J.M.: Introduction to modern information retrieval (1983)

    Google Scholar 

14. Baldi, P., Frasconi, P., Smyth, P.: Modeling the Internet and the Web, Probabilistic Methods and Algorithms. Wiley (2003)

    Google Scholar 

15. Robertson, S.E.: The probability ranking principle in IR. Journal of the American Society for Information Science (33), 294–304 (1997)

    Google Scholar 

16. Robertson, S.E., Maron, M.E., Cooper, W.S.: Probability of relevance: a unification of two competing models for document retrieval. Information Technology: Research and Development (1), 1–21 (1982)

    Google Scholar 

17. Robertson, S.E.: Understanding inverse document frequency: on theoretical arguments for IDF. Journal of Documentation 60(5), 503–520 (2004)

    Article  Google Scholar 

18. Robertson, S.E.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–21 (1972)

    Article  Google Scholar 

19. Hotho, A., Nurnberger, A., Paab, G.: A brief survey of text mining. LDV Forum - GLDV Journal for Computational Linguistics and Language Technology 20(19-62) (2005)

    Google Scholar 

20. Cheng, T.W., Goldgof, D.B., Hall, L.O.: Fast fuzzy clustering. Fuzzy Sets and Systems (93), 49–56 (1998)

    Article  MATH  Google Scholar 

21. Yang, M.-S., Tsai, H.-S.: A gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognition Letters (29), 1713–1725 (2008)

    Article  Google Scholar 

22. Cortez, E., Park, S., Kim, S.: The hybrid application of an inductive learning method and a neural network for intelligent information retrieval. Information Processing & Management 31(6), 789–813 (1995)

    Article  Google Scholar 

23. Lam, W., Ho, C.: Using a generalized instance set for automatic text categorization. In: SIGIR 1998: 21st ACM Int. Conf. on Research and Development in Information Retrieval, pp. 81–89 (1998)

    Google Scholar 

24. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys, 1–47 (2002)

    Google Scholar 

25. Dziwiñski, P., Rutkowska, D.: Algorithm for Generating Fuzzy Rules for WWW Document Classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1111–1119. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

26. Handl, J., Meyer, B.: Improved Ant-Based Clustering and Sorting. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN VII. LNCS, vol. 2439, pp. 913–923. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

27. Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. 25th Anniversary Machine Learning 85(25), 1–42 (2011)

    Article  Google Scholar 

28. Handl, J., Meyer, B.: Ant-based clustering and topographic mapping. Artificial Life 12(1), 35–61 (2006)

    Article  Google Scholar 

29. Fisher, R.A.: (1936), http://archive.ics.uci.edu/ml/datasets/iris

30. Web-Kb, http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/

31. Web-Kb, http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/

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