Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Evolutionary Computation

International Symposium on Swarm Intelligence and Differential Evolution

EC 2012, SIDE 2012: Swarm and Evolutionary Computation pp 199–205Cite as

Fully Controllable Ant Colony System for Text Data Clustering

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7269))

Abstract

The paper presents a new Fully Controllable Ant Colony Algorithm (FCACA) for the clustering of the text documents in vector space. The proposed new FCACA is a modified version of the Lumer and Faieta Ant Colony Algorithm (LF-ACA). The algorithm introduced new version of the basic heuristic decision function significantly improves the convergence and greater control over the process of the grouping data. The proposed solution was shown in a text example proving efficiency of the proposed solution in comparison with other grouping algorithms.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Piotr Dziwiński, Łukasz Bartczuk & Janusz T. Starczewski

Authors
  1. Piotr Dziwiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Łukasz Bartczuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Janusz T. Starczewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hallz, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dziwiński, P., Bartczuk, Ł., Starczewski, J.T. (2012). Fully Controllable Ant Colony System for Text Data Clustering. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29353-5_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29352-8

  • Online ISBN: 978-3-642-29353-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation