Abstract
The paper presents results of research on the clustering problem on the basis of swarm intelligence using a new algorithm based on the normalized cumulative distribution function of attributes. In this approach, we assume that the analysis of likelihood of the occurrence of particular types of attributes and their values allows us to measure the similarity of the objects within a given category and the dissimilarity of the objects between categories. Therefore, on the basis of the complex data set of attributes of any type, we can successfully raise a lot of interesting information about these attributes without necessity of considering their real meaning. Our research shows that the algorithm inspired by the mechanisms observed in nature may return better results due to the modification of the neighborhood based on the similarity coefficient.
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References
Abbass, H., Hoai, N., McKay, R.: AntTAG: A new method to compose computer programs using colonies of ants. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu (2002)
Azzag, H., Monmarche, N., Slimane, M., Venturini, G.: AntTree: a new model for clustering with artificial ants. In: Proceedings of the 2003 Congress on Evolutionary Computation, Beijing, China, pp. 2642–2647 (2003)
Berkhin, P.: Survey of clustering data mining techniques. Tech. rep. Accrue Software, Inc., San Jose, California (2002)
Bin, W., Zhongzi, S.: A clustering algorithm based on swarm intelligence. In: Proceedings of 2001 International Conferences on Info-tech and Info-net, Beijing, China, pp. 58–66 (2001)
Boryczka, U.: Ant clustering algorithm. In: Proceedings of the Conference on Intelligent Information Systems, Zakopane, Poland, pp. 377–386 (2008)
Deneubourg, J., Goss, 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 Behaviour: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)
Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3(3), 32–57 (1973)
Han, Y., Shi, P.: An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70(4-6), 665–671 (2007)
Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artificial Life 12(1), 35–62 (2006)
Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering: a comparative study of its relative performance with respect to k-means, average link and 1d-som. Tech. rep., IRIDIA (2003)
Handl, J., Knowles, J., Dorigo, M.: Strategies for the Increased Robustness of Ant-based Clustering. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS (LNAI), vol. 2977, pp. 90–104. Springer, Heidelberg (2004)
Lewicki, A.: Generalized non-extensive thermodynamics to the ant colony system. In: Świa̧tek, J., Borzemski, L., Grzech, A., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010)
Lewicki, A.: Non-euclidean metric in multi-objective ant colony optimization algorithms. In: Świa̧tek, J., Borzemski, L., Grzech, A., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010)
Lewicki, A., Tadeusiewicz, R.: The recruitment and selection of staff problem with an ant colony system. In: Proceedings of the 3rd International Conference on Human System Interaction, Rzeszów, Poland, pp. 770–774 (2010)
Lewicki, A., Tadeusiewicz, R.: An Autocatalytic Emergence Swarm Algorithm in the Decision-Making Task of Managing the Process of Creation of Intellectual Capital. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human – Computer Systems Interaction, Part I. AISC, vol. 98, pp. 271–285. Springer, Heidelberg (2012)
Lewicki, A., Pancerz, K., Tadeusiewicz, R.: The Use of Strategies of Normalized Correlation in the Ant-Based Clustering Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 637–644. Springer, Heidelberg (2011)
Ouadfel, S., Batouche, M.: An efficient ant algorithm for swarm-based image clustering. Journal of Computer Science 3(3), 162–167 (2007)
Rand, W.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)
van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)
Scholes, S., Wilson, M., Sendova-Franks, A.B., Melhuish, C.: Comparisons in evolution and engineering: The collective intelligence of sorting. Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems 12(3-4), 147–159 (2004)
Vizine, A., de Castro, L., Hruschka, E., Gudwin, R.: Towards improving clustering ants: An adaptive ant clustering algorithm. Informatica 29(2), 143–154 (2005)
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Lewicki, A., Pancerz, K., Tadeusiewicz, R. (2013). Ant Colony Inspired Clustering Based on the Distribution Function of the Similarity of Attributes. In: Nguyen, N., Trawiński, B., Katarzyniak, R., Jo, GS. (eds) Advanced Methods for Computational Collective Intelligence. Studies in Computational Intelligence, vol 457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34300-1_14
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DOI: https://doi.org/10.1007/978-3-642-34300-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34299-8
Online ISBN: 978-3-642-34300-1
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