http://prlab.ee.memphis.edu/frigui/cluster_paper.html
Clustering Papers
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Survey & Tutorial Papers
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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.
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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",
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Clustering Algorithms for Spatial Databases: A Survey ,
Erica Kolatch, Dept. of Computer Science, University of Maryland, College Park.
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BIRCH
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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.
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BIRCH: A New Data Clustering Algorithm and Its Applications,
T. Zhang, R. Ramakrishnan and M. Livny, Kluwer Academic Publishers, Boston.
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Source Code
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local copy of the code
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CURE
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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.
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long version (PS)
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Source Code
(provided by Eui-Hong (Sam) Han, Dept. of Comp. Science & Eng. Univ. of Minnesota; han@cs.umn.edu)]
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CLARANS
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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.
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DBSCAN
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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
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ScaleKM and ScaleEM
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Scaling Clustering Algorithms to Large Databases ,
P. S. Bradley and Usama M. Fayyad and Cory Reina, Knowledge Discovery and Data Mining, 1998.
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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.
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MAFIA
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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.
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CHAMELEON
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CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling.
George Karypis and Eui-Hong (Sam) Han and Vipin Kumar. Computer Vol. 32, No. 8, 1999.
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ROCK
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ROCK: a robust clustering algorithm for categorical attributes .
S. Guha, R. Rastogi and K. Shim. In Proceedings of International Conference on Data Engineering, 1999.
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WaveCluster
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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.
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STING
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STING : A Statistical Information Grid Approach to Spatial Data Mining.
Wei Wang and Jiong Yang and Richard R. Muntz. The {VLDB} Journal, 1997.
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STING+: An Approach to Active Spatial Data Mining.
Wei Wang and Jiong Yang and Richard R. Muntz. ICDE, 1999.
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DENCLUE
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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.
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OPTICS
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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.
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ENCLUS