Abstract
Clustering analysis is an important data mining technique with a variety of applications. In this paper, the data set is treated in a dynamic way and a Data Set Homeomorphism Transformation Based Meta-Clustering algorithm (DSHTBMC) is proposed. DSHTBMC decomposes the task of clustering into multiple stages. It firstly constructs a series of homeomorphous data sets ranging from high regularity to low, and then iteratively clusters each homeomorphism data set based on the clustering result of the preceding homeomorphism data set. Since data sets of high regularities are easier to be clustered, and the clustering result of each homeomorphism data set can be used to induce high quality clusters in the following-up homeomorphism data set, in this way, the hardness of the problem is decreased. Two strategies (i.e., Displacement and Noising) for data set homeomorphism transformation are proposed, with classical hierarchical divisive method–Bisecting k-means as DSHTBMC’s subordinate clustering algorithm, two new clustering algorithms-—HD-DSHTBMC-D and HD-DSHTBMC-N are obtained. Experimental results indicate that the new clustering algorithms are remarkably better than Bisecting k-means algorithm in terms of clustering quality.
This paper is supported by National of Science Foundation of China under grant number: 60503003.
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Berkhin, P.: Survey of Clustering Data Mining Techniques. Technical report, Accrue Software (2002)
Qian, W.-N., Zhou, A.-Y.: Analyzing Popular Clustering Algorithms from Different Viewpoints. Journal of Software, 1382–1394 (2002)
Hartigan, J., Wong, M.: A K-means Clustering Algorithm. Applied Statistics, 100–108 (1979)
Ng, R.T., Han, J.: Efficient and Effective Clustering Methods for Spatial Data Mining. In: Proceeding of the 20th VLDB Conference Santiago, Chile, pp. 144–155 (1994)
Ordonez, C., Omiecinski, E.: FREM: Fast and Robust EM Clustering for Large Data Sets. In: ACM CIKM Conference, pp. 590–599 (2002), http://citeseer.ist.psu.edu/536108.html
Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER, 68–75 (1999)
Zhang, T., Ramakrishna, R., Livny, M.: BIRCH: A New Data Clustering Algorithm and its Applications. Journal of Data Mining and Knowledge Discovery, 141–182 (1997)
Boley, D.L.: Principal Direction Divisive Partitioning. Technical Report TR-97-056, Dept. of Computer Science, University of Minnesota, Minneapolis, to appear in Data Mining and Knowledge Discovery (1997)
Boley, D.L.: Principal Direction Divisive Partitioning. Data Mining and Knowledge Discovery, 325–344 (1998)
Sheikholeslami, G., Chatterjee, S., Zhang, A.: WaveCluster: A Multi-resolution Clustering Approach for Very Large Spatial Databases. In: Proceedings of the 24th Conference on VLDB, New York, NY, pp. 428–439 (1998)
Wang, W., Yang, J., Muntz, R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd Conference on VLDB, Athens, Greece, pp. 186–195 (1997)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: Proc.1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98), Seattle, WA, June 1998, pp. 94–105 (1998)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial database. In: Proc. 1996 Int. Conf.Knowledge Discovery and Data Mining (KDD’96), Portland, OR, Aug. 1996, pp. 226–231 (1996)
Ankerst, M., Breunig, M., Kriegel, H.P., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proc. 1999 ACM-SIGMOD Int.Conf Management of Data (SIGMOD’99), Philadelphia, PA, June 1999, pp. 49–60 (1999)
Savaresi, S.M., Boley, D.L.: On Performance of Bisecting k-means and PDDP. In: Proceedings of the 1st SIAM ICDM, Chicago, IL, pp. 1–14 (2001)
Savaresi, S.M., et al.: Choosing the Cluster to Split in Bisecting Divisive Clustering Algorithms. CSE Report TR 00-055, University of Minnesota (2000)
Lin, J.: Basic of Topology, pp. 19–22. Science Press, Beijing (2003)
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Zhang, X., Zong, Y., Jiang, H., Liu, X. (2007). Data Set Homeomorphism Transformation Based Meta-clustering. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72588-6_112
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