A Multiple Pheromone Ant Clustering Algorithm

Part of the Studies in Computational Intelligence book series (SCI, volume 512)

Abstract

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis.

Keywords

Ant Colony Algorithms Swarm Intelligence Emergent Behaviour Cluster Analysis Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    French, J.R.J., Ahmed, B.M.: The challenge of biomimetic design for carbon-neutral buildings using termite engineering. InsectScience 17(2), 154–162 (2010)Google Scholar
  2. 2.
    Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2013), http://archive.ics.uci.edu/ml Google Scholar
  3. 3.
    Guerona, S., Levin, S.A., Rubenstein, D.I.: The dynamics of herds: From Individuals to Aggregations. Journal of Theoretical Biology 182, 85–89 (1996)CrossRefGoogle Scholar
  4. 4.
    Parrish, J.K., Hamner, W.M.: Animal Groups in Three Dimensions, How Species Aggregate. Cambridge University Press (1997)Google Scholar
  5. 5.
    Murray, J.D.: Mathematical Biology. Springer, New York (1989)MATHCrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization, vol. 1, pp. 28–39 (November 2006)Google Scholar
  7. 7.
    Deneubourg, J.L., Gross, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp. 356–363 (1990)Google Scholar
  8. 8.
    Dorigo, M.: Optimisation, Learning, and Natural Algorithms. Ph.D. Thesis. Dipartimento Di Elettronica, Politecnico Di Milano, Milan, Italy (1992)Google Scholar
  9. 9.
    Dussutour, A., Nicolis, S.C., Shephard, G., Beekman, M., Sumpter, D.J.T.: The role of multiple pheromones in food recruitment by ants. The Journal of Experimental Biology 212(4), 2337–2348 (2009)CrossRefGoogle Scholar
  10. 10.
    Ngenkaew, W., Ono, S., Nakayama, S.: Pheromone-based concept in Ant Clustering. In: 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008, Xiamen, November 17-19, vol. 1, pp. 308–312 (2008)Google Scholar
  11. 11.
    Middendorf, M., Reischle, F., Schmeck, H.: Multi Colony Ant Algorithms. Journal of Heuristics 8(3), 305–320 (2002), http://dx.doi.org/10.1023/A:1015057701750, doi:10.1023/A:1015057701750MATHCrossRefGoogle Scholar
  12. 12.
    Guntsch, M.: Ant Algorithms in Stochastic and Multi-Criteria Environments (2004)Google Scholar
  13. 13.
    Jafar, O.A.M., Sivakumar, R.: Ant-based Clustering Algorithms: A Brief Survey. International Journal of Computer Theory and Engineering 2(5), 1793–8201 (2010), http://www.ijcte.org/papers/242-G730.pdf Google Scholar
  14. 14.
    Labroche, N., Monmarché, N., Venturini, G.: A New Clustering Algorithm Based on the Chemical Recognition System of Ants. In: Proc. of 15th European Conference on Artificial Intelligence (ECAI 2002), Lyon, France, pp. 345–349 (2002)Google Scholar
  15. 15.
    Labroche, N., Richard, F.J., Monmarché, N., Lenoir, A., Venturini, G.: Modelling of the Chemical Recognition System of AntsGoogle Scholar
  16. 16.
    Zaharie, D., Zamfirache, F.: Dealing with noise in ant-based clustering. In: The 2005 IEEE Congress on Evolutionary Computation, September 2-5, vol. 3, pp. 2395–2401 (2005)Google Scholar
  17. 17.
    Liang, X.-C., Chen, S.-F., Liu, Y.: The study of small enterprises credit evaluation based on incremental AntClust. In: IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2007, Nanjing, November 18-20, pp. 294–298 (2007)Google Scholar
  18. 18.
    Inbarani, H.H., Thangavel, K.: Clickstream Intelligent Clustering using Accelerated Ant Colony Algorithm. In: International Conference on Advanced Computing and Communications, ADCOM 2006, December 20-23, pp. 129–134 (2006)Google Scholar
  19. 19.
    Bertelle, C., Dutot, A., Guinand, F., Olivier, D.: Organization Detection Using Emergent Computing. International Transactions on Systems Science and Applications (ITSSA) 2(1), 61–69 (2006)Google Scholar
  20. 20.
    Ramos, V., Muge, F., Pina, P.: Self-Organized Data and Image Retrieval as a Consequence of Inter-DynamicSynergistic Relationships in Artificial Ant Colonies. In: Hybrid Intelligent Systems, Frontiers of Artificial Intelligence and Applications, AEB 2002, vol. 87, pp. 500–509 (December 2002)Google Scholar
  21. 21.
    El-Feghi, I., Errateeb, M., Ahmadi, M., Sid-Ahmed, M.A.: An adaptive ant-based clustering algorithm with improved environment perception. In: IEEE International Conference on Systems Man and Cybernetics Systems, SMC 2009, San Antonio, TX, October 11-14, pp. 1431–1438 (2009)Google Scholar
  22. 22.
    Kothari, M., Ghosh, S., Ghosh, A.: Aggregation Pheromone Density Based Clustering. In: 9th International Conference on Information Technology, ICIT 2006, Bhubaneswar, December 18-21, pp. 259–264 (2006)Google Scholar
  23. 23.
    Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Analytica Chimica Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar
  24. 24.
    Jiang, H., Chen, S.: A new ant colony algorithm for a general clustering. In: IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2007, Nanjing, November 18-20, pp. 1158–1162 (2007)Google Scholar
  25. 25.
    Yang, H., Li, X., Bo, C., Shao, X.: A Graphic Clustering Algorithm Based on MMAS. In: IEEE Congress on Evolutionary Computation, CEC 2006, Vancouver, BC, September 11, pp. 1592–1597 (2006)Google Scholar
  26. 26.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4), 321–332 (2002)CrossRefGoogle Scholar
  27. 27.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: An Ant Colony Based System for Data Mining: Applications To Medical Data. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 791–797 (2001)Google Scholar
  28. 28.
    Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification With Ant Colony Optimization. IEEE Transactions on Evolutionary Computation 11(5), 651–665 (2007); Sponsored by : IEEE Computational Intelligence SocietyGoogle Scholar
  29. 29.
    Elizondo, D.: The Linear Separability Problem: Some Testing Methods. IEEE Transactions on Neural Networks 17(2), 330–344 (2006)CrossRefGoogle Scholar
  30. 30.
    Handl, J., Knowles, J., Dorigo, M.: On the performance of ant-based clustering. In: Proceedings of the Third International Conference on Hybrid Intelligent Systems Frontiers in Artificial Intelligence and Appliations, vol. 104, pp. 204–213 (2003)Google Scholar
  31. 31.
    Sasaki, Y.: The truth of the F-measure, http://www.toyota-ti.ac.jp/Lab/Denshi/COIN/people/yutaka.sasaki/index-e.html (accessed June 30, 2011)
  32. 32.
    Li, L., Wu, W.-C., Rong, Q.-M.: Research on Hybrid Clustering Based on Density and Ant Colony Algorithm. In: 2010 Second International Workshop on Education Technology and Computer Science (ETCS), Wuhan, March 6-7, vol. 2, pp. 222–225 (2010)Google Scholar
  33. 33.
    Mahmoodi, M.S., Bigham, B.S., Khan Rostam, A.N.-A., Mahmoodi, S.A.: Using Fuzzy Classification Sysstem for Diagnosis of Breast Cancer. In: CICIS 2012, IASBS, Zanjan, Iran, May 29-31, pp. 412–417 (2012)Google Scholar
  34. 34.
    Chandrasekar, R., Vijaykumar, V., Srinivasan, T.: Probabilistic Ant based Clustering for Distributed Databases. In: 3rd International IEEE Conference Intelligent Systems, pp. 538–545 (September 2006)Google Scholar
  35. 35.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  36. 36.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996) ISBN 1-57735-004-9Google Scholar
  37. 37.
    Xiong, Z., Chen, R., Zhang, Y., Zhang, X.: Multi-density DBSCAN Algorithm Based on Density Levels Partitioning. Journal of Information and Computational Science 9(10), 2739–2749 (2012)Google Scholar
  38. 38.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar
  39. 39.
    Buckingham, C.D., Ahmed, A., Adams, A.E.: Using XML and XSLT for flexible elicitation of mental-health risk knowledge. Medical Informatics and the Internet in Medicine 32(1), 65–81 (2007)CrossRefGoogle Scholar
  40. 40.
    Buckingham, C.D., Buijs, P., Welch, P.G., Kumar, A., Ahmed, A.: Developing a cognitive model of decision-making to support members of hub-and-spoke logistics networks. In: Ilie-Zudor, E., Kemény, Z., Monostori, L. (eds.) Proceedings of the 14th International Conference on Modern Information Technology in the Innovation Processes of the Industrial Enterprises. Hungarian Academy of Sciences, Computer and Automation Research Institute, pp. 14–30 (2012), igor.xen.emi.sztaki.hu/mitip/media/MITIP2012proceedings.pdf

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Aston UniversityBirminghamUnited Kingdom

Personalised recommendations