Abstract
Classical clustering algorithms require a predefined number of cluster centers. They are often very sensitive to initialization, which can result in very different clustering results. We present a two-phase algorithm which is a combination of a new ant based algorithm and a nonnegative matrix factorization-based consensus clustering algorithm. Ant clustering approaches can and do find the number of clusters as well as the data partition. However, they are very sensitive to both initial conditions and select parameters. Here, we show that using an ensemble of ant partitions and NMF to combine them we can find both the “right” number of clusters and a good data partition. Experiments were done with ten data sets. We conducted a wide range of comparisons that demonstrate the effectiveness of this new approach.
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References
Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a Genetically Optimized Approach. IEEE Transactions on Evolutionary Computation 3(2), 103–112 (1999)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice Hall, Upper Saddle River (2003)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. Prentice Hall of India Private Limited, New Delhi (2005)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transaction on SMC-part B 26(1), 29–41 (1996)
Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A.: Detrain: The dynamics of collective sorting: robot-like ants and ant-like robots. In: Meyer, J.A., Wilson, S. (eds.) Proc. of the first Intern. Conf. on Simulation of Adaptive Behaviour: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)
Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proc. of the third Interm. Conf. on Simulation of Adaptive Behavior: from Animals to Animats 3, pp. 501–508. MIT Press, Cambridge (1994)
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, vol. 2977, pp. 90–104. Springer, Heidelberg (2004)
Labroche, N., Monmarche, N., Venturini, G.: A New Clustering Algorithm Based on the Chemical Recognition System of Ants. In: van Harmelen, F. (ed.) Proc. of the 15th European Conference on Artificial Intelligence, Lyon, France, pp. 345–349 (2002)
Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: ICML (2004)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. In: ICDE, pp. 341–352 (2005)
Hu, X., Yoo, I., Zhang, X., Nanavati, P., Das, D.: Wavelet transformation and cluster ensemble for gene expression analysis. International Journal of Bioinformatics Research and Application 1(4), 447–460 (2006)
Li, T., Ding, C.: Weighted Consensus Clustering. In: Proceedings of the 2008 SIAM International Conference on Data Mining, Atlanta, April 24-26 (2008)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix tri-factorizations for clustering. In: SIGKDD, pp. 126–135 (2006)
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research (JMLR) 3, 583–617 (2002)
Lee, D.D., Seung, H.S.: Learning the parts of objects with nonnegative matrix factorization. Nature 401, 788–791 (1999)
Xu, W., et al.: Document Clustering Based on Non-Negative Matrix Factorization. In: SIGIR, pp. 267–273 (2003)
Blake, C.L., Newman, D.J., Hettich, S., Merz, C.J.: UCI repository of machine learning databases (1998)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From natural to artificial systems. Oxford University Press, New York (1999)
Hall, L.O., Kanade, P.M.: Swarm Based Fuzzy Clustering with partition Validity. In: FUZZ 2005. The 14th IEEE International Conference on Fuzzy Systems, 2005, May 22-25, pp. 991–995 (2005)
Zaharie, D., Zamfirache, F.: Dealing with noise in ant-based clustering. In: Proc. IEEE Congress of Evolutionary Computation 2005, Edinburgh, pp. 2395–2402 (2005)
Brunet, J., Tamayo, P., Golub, T., Mesirov, J.: Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the National Academy of Sciences 101(12), 4164–4169 (2004)
Kurita, T.: An efficient agglomerative clustering algorithm for region growing. In: Proc. of IAPR Workshop on Machine Vision Applications, pp. 210–213 (1994)
Gu, Y., Hall, L.O., Goldgof, D.B.: Ant Clustering with consensus, Technical Report ISL-1-2009, http://www.cse.usf.edu/~hall/papers/tr109.pdf
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Gu, Y., Hall, L.O., Goldgof, D.B. (2009). Ant Clustering Using Ensembles of Partitions. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_29
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DOI: https://doi.org/10.1007/978-3-642-02326-2_29
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