Cluster Analysis

  1. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
  2. R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. In VLDB′94, pp. 144-155, Santiago, Chile, Sept. 1994.
  3. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In SIGMOD′96, pp. 103-114, Montreal, Canada, June 1996.
  4. E. Schikuta. Grid clustering: An efficient hierarchical clustering method for very large data sets. Proc. 1996 Int. Conf. on Pattern Recognition, 101-105. (citeseer)
  5. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In KDD′96, pp. 226-231, Portland, Oregon, August 1996.
  6. W. Wang, Yang, R. Muntz, STING: A Statistical Information grid Approach to Spatial Data Mining, VLDB’97, 1997. (citeseer)
  7. S. Guha, R. Rastogi, and K. Shim. CURE: An efficient clustering algorithm for large databases. In SIGMOD′98, pp. 73-84, Seattle, Washington, June 1998.
  8. S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categorical attributes. In ICDE′99, pp. 512-521, Sydney, Australia, March 1999.
  9. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD′98, pp. 94-105, Seattle, Washington, June 1998.
  10. Alexander Hinneburg, Daniel A. Keim: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. KDD 1998: 58-65, 1998. (citeseer)
  11. G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In VLDB′98, pp. 428-439, New York, NY, August 1998.
  12. D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An approach based on dynamic systems. In Proc. VLDB’98. (citeseer)
  13. G. Karypis, E.-H. Han, and V. Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER, 32(8): 68-75, 1999.
  14. Wei Wang, Jiong Yang, Richard Muntz. STING+: an approach to active spatial data mining. ICDE 99, pp. 116-125. 1999. (citeseer)
  15. M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering points to identify the clustering structure. In SIGMOD′99, pp. 49-60, Philadelphia, PA, June 1999.
  16. V. Ganti, J. Gehrke, R. Ramakrishan. CACTUS Clustering Categorical Data Using Summaries. Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD′99), San Diego, CA, 261-270, Aug. 1999. (citeseer) (Journal version: citeseer)
  17. M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander. LOF: Identifying Density-Based Local Outliers. In Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2000), Dallas, TX, 2000, pp. 93-104. (citeseer)
  18. A. K. H. Tung, J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-Based Clustering in Large Databases , Proc. 2001 Int. Conf. on Database Theory (ICDT′01), London, U.K., Jan. 2001.
  19. A. K. H. Tung, J. Hou, and J. Han. Spatial Clustering in the Presence of Obstacles , Proc. 2001 Int. Conf. on Data Engineering (ICDE′01), Heidelberg, Germany, April 2001
  20. H. Wang, W. Wang, J. Yang, and P.S. Yu.  Clustering by pattern similarity in large data setsProc. the ACM SIGMOD International Conference on Management of Data (SIGMOD), Madison, Wisconsin, 2002.
  21. Beil F., Ester M., Xu X.: "Frequent Term-Based Text Clustering", Proc. 8th Int. Conf. on Knowledge Discovery and Data Mining (KDD′02), Edmonton, Alberta, Canada, 2002.
  22. L. Parsons, E. Haque and H. Liu, Subspace Clustering for High Dimensional Data: A Review , SIGKDD Explorations, Vol. 6(1), June 2004
  23. Samer Nassar, Jörg Sander, Corrine Cheng, Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering, SIGMOD’04
  24. Sugato Basu, Mikhail Bilenko, Raymond Mooney, A Probabilistic Framework for Semi-Supervised Clustering, Proc.  2004 ACM-SIGKDD Int. Conf. on Management of Data (KDD′04), Seattle, WA, Aug. 2004
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