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.
[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 , ,
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, ,
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
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.
Gholamhosein Sheikholeslami and Surojit Chatterjee and Aidong Zhang. Proc. 24th Int. Conf. Very Large Data Bases.
[PDF]STING
DENCLUE
An Efficient Approach to Clustering in Multimedia Databases with Noise.
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
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
Demo: ( Top)
- Robust & Competitive Clustering Demo 1
- Clustering Demo 2 Currently Down
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