Abstract
We propose an adaptive ant colony data clustering algorithm for a dynamic database. The algorithm uses a digraph where the vertices represent the data to be clustered. The weight of the edge represents the acceptance rate between the two data connected by the edge. The pheromone on the edges is adaptively updated by the ants passing through it. Some edges with less pheromone are progressively removed under a list of thresholds in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that the algorithm can find clusters more quickly and with better quality than K-means and LF. In addition, when the database is changed, the algorithm can dynamically modify the clusters accordingly to maintain its accuracy.
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Kaufman, L., Pierreux, A., Rousseuw, P., Derde, M.P., Detaecernier, M.R., Massart, D.L., Platbrood, G.: Clustering on a Microcomputer with an Application to the Classification of Coals. Analytica Chimica Acta 153, 257–260 (1983)
Lawson, R.G., Jurs, P.C.: Cluster Analysis of Acrylates to Guide Sampling for Toxicity Testing. Journal of Chemical Information and Computer Science 30(1), 137–144 (1990)
Beckers, M.L.M., Melssen, W.J., Buydens, L.M.C.: A self-organizing feature map for clustering nucleic acids. Application to a data matrix containing A-DNA and B-DNA dinucleotides. Comput. Chem. 21, 377–390 (1997)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Bonabeau, E., Dorigo, M., Théraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. In: Santa Fe Institute in the Sciences of the Complexity. Oxford University Press, Oxford (1999)
Dorigo, M., Maniezzo, V., Colomi, A.: Ant system: Optimization by a colony of coorperating agents. IEEE Transactions on Systems, Man and Cybernetics-Part B 26(1), 29–41 (1996)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation 1(1), 53–66 (1997)
Stutzle, T., Hoos, H.: MAX-MIN Ant systems. Future Generation Computer Sytems 16, 889–914 (2000)
Dorigo, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)
Chang, C.S., Tian, L., Wen, F.S.: A new approach to fault section in power systems using Ant System. Electric Power Systems Research 49(1), 63–70 (1999)
Gambardella, L.M., Dorigo, M.: HAS-SOP: An Hybrid Ant System for the Sequential Ordering Problem. Tech. Rep. No. IDSIA 97-11, IDSIA, Lugano Switzerland (1997)
Kuntz, P., Layzell, P., Snyder, D.: A colony of ant-like agents for partitioning in VLSI technology. In: Husbands, P., Harvey, I. (eds.) Proceedings of the Fourth European Conference on Artificial Life, pp. 412–424. MIT Press, Cambridge (1997)
Kuntz, P., Snyder, D.: New results on ant-based heuristic for highlighting the organization of large graphs. In: Proceedings of the 1999 Congress or Evolutionary Computation, pp. 1451–1458. IEEE Press, Piscataway (1999)
Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The Dynamic of Collective Sorting Robot-like Ants and Ant-like Robots. In: Meyer, J.A., Wilson, S.W. (eds.) SAB 1990-1st Conf. On Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–365. MIT Press, Cambridge (1991)
Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animates, vol. 3, pp. 501–508. MIT Press/ Bradford Books, Cambridge (1994)
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Chen, L., Tu, L., Chen, Y. (2006). An Ant Clustering Method for a Dynamic Database. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_18
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DOI: https://doi.org/10.1007/11739685_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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