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Volumn , Issue , 2005, Pages 346-353

Parameter-free spatial data mining using MDL

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; DATA ACQUISITION; DATA COMPRESSION; DATA REDUCTION; DATABASE SYSTEMS; PARAMETER ESTIMATION;

EID: 34548569508     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.117     Document Type: Conference Paper
Times cited : (14)

References (32)
  • 1
    • 0002221136 scopus 로고
    • Fast algorithms for mining association rules in large databases
    • R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, 1994.
    • (1994) VLDB
    • Agrawal, R.1    Srikant, R.2
  • 3
    • 12244300524 scopus 로고    scopus 로고
    • S. Basu, M. Bilenko, and R. J. Mooney. A probabilistic framework for semi-supervised clustering. In KDD, 2004.
    • S. Basu, M. Bilenko, and R. J. Mooney. A probabilistic framework for semi-supervised clustering. In KDD, 2004.
  • 4
    • 12244296737 scopus 로고    scopus 로고
    • D. Chakrabarti, S. Papadimitriou, D. Modha, and C. Faloutsos. Fully automatic cross-associations. In KDD, 2004.
    • D. Chakrabarti, S. Papadimitriou, D. Modha, and C. Faloutsos. Fully automatic cross-associations. In KDD, 2004.
  • 6
    • 77952375075 scopus 로고    scopus 로고
    • I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In KDD, 2003.
    • I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In KDD, 2003.
  • 7
    • 0034824884 scopus 로고    scopus 로고
    • Concept decompositions for large sparse text data using clustering
    • I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Mach. Learning, 42, 2001.
    • (2001) Mach. Learning , vol.42
    • Dhillon, I.S.1    Modha, D.S.2
  • 9
    • 0032091595 scopus 로고    scopus 로고
    • CURE: An efficient clustering algorithm for large databases
    • S. Guha, R. Rastogi, and K. Shim. CURE: an efficient clustering algorithm for large databases. In SIGMOD, 1998.
    • (1998) SIGMOD
    • Guha, S.1    Rastogi, R.2    Shim, K.3
  • 10
    • 9144231916 scopus 로고    scopus 로고
    • Learning the k in k-means
    • G. Hamerly and C. Elkan. Learning the k in k-means. In NIPS, 2003.
    • (2003) NIPS
    • Hamerly, G.1    Elkan, C.2
  • 12
    • 2442449952 scopus 로고    scopus 로고
    • Mining frequent patterns without candidate generation: A frequent-pattern tree approach
    • J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53-87, 2004.
    • (2004) Data Min. Knowl. Discov , vol.8 , Issue.1 , pp. 53-87
    • Han, J.1    Pei, J.2    Yin, Y.3    Mao, R.4
  • 14
    • 34548596320 scopus 로고    scopus 로고
    • A. Hinneburg and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. In KDD, 1998.
    • A. Hinneburg and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. In KDD, 1998.
  • 15
    • 0038675380 scopus 로고    scopus 로고
    • Y. Huang, H. Xiong, S. Shekhar, and J. Pei. Mining confident co-location rules without a support threshold. In SAC, 2003.
    • Y. Huang, H. Xiong, S. Shekhar, and J. Pei. Mining confident co-location rules without a support threshold. In SAC, 2003.
  • 16
    • 0032686723 scopus 로고    scopus 로고
    • Chameleon: Hierarchical clustering using dynamic modeling
    • G. Karypis, E.-H. Han, and V. Kumar. Chameleon: Hierarchical clustering using dynamic modeling. IEEE Computer, 32(8), 1999.
    • (1999) IEEE Computer , vol.32 , Issue.8
    • Karypis, G.1    Han, E.-H.2    Kumar, V.3
  • 17
    • 34548563509 scopus 로고    scopus 로고
    • G. Karypis and V. Kumar. Multilevel algorithms for multi-constraint graph partitioning. In SC98, 1998.
    • G. Karypis and V. Kumar. Multilevel algorithms for multi-constraint graph partitioning. In SC98, 1998.
  • 18
    • 10644281769 scopus 로고    scopus 로고
    • E. Keogh, S. Lonardi, and C. A. Ratanamahatana. Towards parameter-free data mining. In KDD, 2004.
    • E. Keogh, S. Lonardi, and C. A. Ratanamahatana. Towards parameter-free data mining. In KDD, 2004.
  • 19
    • 1842545358 scopus 로고    scopus 로고
    • Approximation algorithms for classification problems with pairwise relationships: Metric labeling and Markov random fields
    • J. Kleinberg and E. Tardos. Approximation algorithms for classification problems with pairwise relationships: Metric labeling and Markov random fields. JACM, 49(5):616-639, 2002.
    • (2002) JACM , vol.49 , Issue.5 , pp. 616-639
    • Kleinberg, J.1    Tardos, E.2
  • 20
    • 34548565375 scopus 로고    scopus 로고
    • Rule discovery and probabilistic modeling for onomastic data
    • A. Leino, H. Mannila, and R. L. Pitkänen. Rule discovery and probabilistic modeling for onomastic data. In PKDD, 2003.
    • (2003) PKDD
    • Leino, A.1    Mannila, H.2    Pitkänen, R.L.3
  • 21
    • 12244250580 scopus 로고    scopus 로고
    • On finding large conjunctive clusters
    • N. Mishra, D. Ron, and R. Swaminathan. On finding large conjunctive clusters. In COLT, 2003.
    • (2003) COLT
    • Mishra, N.1    Ron, D.2    Swaminathan, R.3
  • 22
    • 0041875229 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, 2001.
    • (2001) NIPS
    • Ng, A.Y.1    Jordan, M.I.2    Weiss, Y.3
  • 23
    • 0001820920 scopus 로고    scopus 로고
    • X-means: Extending K-means with efficient estimation of the number of clusters
    • D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. In ICML, pages 727-734, 2000.
    • (2000) ICML , pp. 727-734
    • Pelleg, D.1    Moore, A.2
  • 24
    • 84926736518 scopus 로고
    • Some generalized order-disorder transformations
    • R. B. Potts. Some generalized order-disorder transformations. Proc. Camb. Phil. Soc., 48:106, 1952.
    • (1952) Proc. Camb. Phil. Soc , vol.48 , pp. 106
    • Potts, R.B.1
  • 25
    • 34548558388 scopus 로고    scopus 로고
    • An approach to relate the web communities through bipartite graphs
    • P. K. Reddy and M. Kitsuregawa. An approach to relate the web communities through bipartite graphs. In WISE, 2001.
    • (2001) WISE
    • Reddy, P.K.1    Kitsuregawa, M.2
  • 27
    • 19544386787 scopus 로고    scopus 로고
    • Evaluating attraction in spatial point patterns with an application in the field of cultural history
    • M. Salmenkivi. Evaluating attraction in spatial point patterns with an application in the field of cultural history. In ICDM, 2004.
    • (2004) ICDM
    • Salmenkivi, M.1
  • 28
    • 0023206186 scopus 로고
    • Variable block-size image coding
    • D. J. Vaisey and A. Gersho. Variable block-size image coding. In ICASSP, 1987.
    • (1987) ICASSP
    • Vaisey, D.J.1    Gersho, A.2
  • 29
    • 5044221833 scopus 로고    scopus 로고
    • Spatially coherent clustering with graph cuts
    • R. Zabih and V. Kolmogorov. Spatially coherent clustering with graph cuts. In CVPR, 2004.
    • (2004) CVPR
    • Zabih, R.1    Kolmogorov, V.2
  • 30
    • 3543074169 scopus 로고    scopus 로고
    • K-harmonic means - a spatial clustering algorithm with boosting
    • B. Zhang, M. Hsu, and U. Dayal. K-harmonic means - a spatial clustering algorithm with boosting. In TSDM, 2000.
    • (2000) TSDM
    • Zhang, B.1    Hsu, M.2    Dayal, U.3
  • 31
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An efficient data clustering method for very large databases
    • T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In SIGMOD, 1996.
    • (1996) SIGMOD
    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3
  • 32
    • 12244312165 scopus 로고    scopus 로고
    • X. Zhang, N. Mamoulis, D. W. Cheung, and Y. Shou. Fast mining of spatial collocations. In KDD, 2004.
    • X. Zhang, N. Mamoulis, D. W. Cheung, and Y. Shou. Fast mining of spatial collocations. In KDD, 2004.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.