聚类论文资源和源代码

• Survey & Tutorial Papers

  • Data Clustering: A review ,
    Anil K. Jain and M. N. Murthy and P. J. Flynn. Pattern Recognition and Image Processing Lab, Department of Computer Science And Engineering, Michigan State University.
    [PDF]
    

  • Tutorial: Clustering Techniques for Large Data Sets: From the Past to the Future. ,
    A. Hinneburg and D. Keim. Tutorial Notes for ACM SIGKDD int. conf. on Knowledge Discovery and Data Mining, 1999",
    [PDF]
    

  • Clustering Algorithms for Spatial Databases: A Survey ,
    Erica Kolatch, Dept. of Computer Science, University of Maryland, College Park.

    数据挖掘实验室

    
    [PDF]
    

• BIRCH

  • BIRCH: An Efficient Data Clustering Method for Very Large Databases ,
    T. Zhang, R. Ramakrishnan and M. Livny, In Proc. of ACM SIGMOD International Conferance on Management of Data, 1996.
    [PDF]
    

  • BIRCH: A New Data Clustering Algorithm and Its Applications,
    T. Zhang, R. Ramakrishnan and M. Livny, Kluwer Academic Publishers, Boston.
    [PDF][Source Code] [local copy of the code]

• CURE

  • CURE: An efficient algorithm for clustering large databases , ,

    数据挖掘研究院

    
    S. Guha, R. Rastogi and K. Shim, n Proceedings of ACM SIGMOD International Conference on Management of Data, pages 73--84, New York, 1998. ACM.
    [ Short version(PDF)] [ long version (PS)] [Source Code (provided by Eui-Hong (Sam) Han, Dept. of Comp. Science & Eng. Univ. of Minnesota; han@cs.umn.edu)]
    

• CLARANS

  • Efficient and Effective Clustering Methods for Spatial Data Mining, ,
    R. T. Ng and J. Han, 20th International Conference on Very Large Data Bases, September 12--15, 1994, Santiago, Chile proceeding.
    [ PDF]
    

• DBSCAN

  • A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, ,

    数据挖掘研究院

    
    Ester M., Kriegel H.-P., Sander J., Xu X., Proc. 2nd Int. Conf.on Knowledge Discovery and Data Mining (KDD′96), Portland, OR, 1996, pp. 226-231
    [ PDF]
    

• ScaleKM and ScaleEM

  • Scaling Clustering Algorithms to Large Databases ,
    P. S. Bradley and Usama M. Fayyad and Cory Reina, Knowledge Discovery and Data Mining, 1998.
    [PDF]
    

  • Scaling EM (Expectation-Maximization) Clustering to Large Databases,
    P. S. Bradley and Usama Fayyad and Cory Reina, Microsoft Research, Tech. Report MSR-TR-98-35.
    [PDF]

• MAFIA

  • MAFIA: Efficient and scalable subspace clustering for very large data sets 数据挖掘实验室
    H. Nagesh S. Goil and A. Choudhary, Technical Report 9906-010, Northwestern University, June 1999.
    [PDF]

• CHAMELEON

  • CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling.
    George Karypis and Eui-Hong (Sam) Han and Vipin Kumar. Computer Vol. 32, No. 8, 1999.
    [PDF]

• ROCK

  • ROCK: a robust clustering algorithm for categorical attributes .
    S. Guha, R. Rastogi and K. Shim. In Proceedings of International Conference on Data Engineering, 1999.
    [PDF]

• WaveCluster

  • WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. 数据挖掘研究院
    Gholamhosein Sheikholeslami and Surojit Chatterjee and Aidong Zhang. Proc. 24th Int. Conf. Very Large Data Bases.
    [PDF]

• STING

  • STING : A Statistical Information Grid Approach to Spatial Data Mining.
    Wei Wang and Jiong Yang and Richard R. Muntz. The {VLDB} Journal, 1997.
    [PDF]

  • STING+: An Approach to Active Spatial Data Mining.
    Wei Wang and Jiong Yang and Richard R. Muntz. ICDE, 1999.
    [PDF]

• DENCLUE

  • An Efficient Approach to Clustering in Multimedia Databases with Noise. 数据挖掘研究院
    Hinneburg A., Keim D.A. Proc. 4rd Int. Conf. on Knowledge Discovery and Data Mining, New York, AAAI Press, 1998.
    [PDF]

• OPTICS

  • OPTICS: Ordering Points To Identify the Clustering Structure, .
    nkerst M., Breunig M. M., Kriegel H.-P., Sander J. Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD′99), Philadelphia, PA, 1999, pp. 49-60.
    [PDF]

• ENCLUS

  • ENCLUS: Entropy-based Subspace Clustering for Mining Numerical Data
    Chun-hung Cheng, Ada Wai-chee Fu, Yi Zhang

    数据挖掘实验室

    
    In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, Aug 1999.
    [PDF][Source Code]

   Source Code: ( Top)
  - BIRCH
  - CLIQUE Link Inactive 数据挖掘研究院
  Demo: ( Top)
  - Robust & Competitive Clustering Demo 1
  - Clustering Demo 2 Currently Down   数据挖掘研究院