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The Role of Randomness in Self-aggregative AntTree Approach

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Cellular Automata (ACRI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9863))

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Abstract

In some research works concerning biomimicry and data mining, new bio-inspired clustering algorithm has been proposed to deal with the difficult problem of a partitioning the data. In this work, a role of randomness in AntTree-based approach is discussed in clustering application. This proposition integrates the random mechanism of inserting ants in the tree representation of partitioning and the concept of attraction of the specific connections in the analyzed structure. In the same time, the role of shoving (dynamically changed) by the dissimilarity between objects has been analyzed. The comparative study concerning ant-based algorithm and the standard DBSCAN approach shows that this proposal achieves results comparable to the best classical approach’s results. This approach shows that randomness improves the results in clustering offered by the AntTree algorithm.

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References

  1. Albuquerque, P., Dupuis, A.: A parallel cellular ant colony algorithm for clustering and sorting. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) ACRI 2002. LNCS, vol. 2493, pp. 220–230. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Azzag, H., Monmarche, N., Slimane, M., Venturini, G., Guinot, C.: AntTree: a new model for clustering with artificial ants. In: IEEE Congress on Evolutionary Computation. IEEE Press (2003)

    Google Scholar 

  3. Bitsakidis, N.P., Chatzichristofis, S.A., Sirakoulis, G.C.: Hybrid cellular ants for clustering problems. Int. J. Unconventional Comput. 11(2), 103–130 (2015)

    Google Scholar 

  4. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  5. Bramer, M.: Principles of Data Mining. Springer, London (2007)

    MATH  Google Scholar 

  6. Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: robot-like ant and ant-like robot. In: Meyer, J.A., Wilson, S.W. (eds.) First Conference on Simulation of Adaptive Behavior. From Animals to Animats, pp. 356–365 (1991)

    Google Scholar 

  7. Garai, G., Chaudhuri, B.B.: A novel genetic algorithm for automatic clustering. Pattern Recogn. Lett. 25(2), 173–187 (2004)

    Article  Google Scholar 

  8. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)

    MATH  Google Scholar 

  9. Kao, Y., Cheng, K.: An ACO-based clustering algorithm. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 340–347. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Karaboga, D., Ozturk, C.: A novel clustering approach: artificial bee colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)

    Article  Google Scholar 

  11. Moere, A.V., Clayden, J.J.: Cellular ants: combining ant-based clustering with cellular automata. In: 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), p. 184, November 2005. 8 pages

    Google Scholar 

  12. Moere, A.V., Clayden, J.J., Dong, A.: Data clustering and visualization using cellular automata ants. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 826–836. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Sheikh, R.H., Raghuwanshi, M.M., Jaiswal, A.N.: Genetic algorithm based clustering: a survey. In: Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 314–319 (2008)

    Google Scholar 

  14. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509, 187–195 (2004)

    Article  Google Scholar 

  15. Tang, N., Vemuri, R.: An artificial immune system approach to document clustering. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 918–922 (2005)

    Google Scholar 

  16. Xu, X., Chen, L., He, P.: A novel ant clustering algorithm based on cellular automata. Web Intell. Agent Syst. Int. J. 5(1), 1–14 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, Sosnowiec, Poland

    Urszula Boryczka & Mariusz Boryczka

Authors
  1. Urszula Boryczka
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  2. Mariusz Boryczka
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Correspondence to Urszula Boryczka .

Editor information

Editors and Affiliations

  1. University of Perpignan, Perpignan, France

    Samira El Yacoubi

  2. AGH University of Science and Techn, Kraków, Poland

    Jaroslaw Wąs

  3. Department of Computer Science, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

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Boryczka, U., Boryczka, M. (2016). The Role of Randomness in Self-aggregative AntTree Approach. In: El Yacoubi, S., Wąs, J., Bandini, S. (eds) Cellular Automata. ACRI 2016. Lecture Notes in Computer Science(), vol 9863. Springer, Cham. https://doi.org/10.1007/978-3-319-44365-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-44365-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44364-5

  • Online ISBN: 978-3-319-44365-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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