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Volumn , Issue , 2004, Pages 186-193

SCHISM: A new approach for interesting subspace mining

Author keywords

[No Author keywords available]

Indexed keywords

BACKTRACKING; CLUSTERING; HIGH DIMENSIONAL DATA; INTRUSION DETECTION;

EID: 19544389465     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10099     Document Type: Conference Paper
Times cited : (79)

References (21)
  • 1
    • 77952412927 scopus 로고    scopus 로고
    • Towards systematic design of distance functions for data mining applications
    • C. Aggarwal. Towards systematic design of distance functions for data mining applications. In SIGKDD Conf, 2003.
    • (2003) SIGKDD Conf
    • Aggarwal, C.1
  • 2
    • 0002681035 scopus 로고    scopus 로고
    • A framework for finding projected clusters in high dimensional spaces
    • C. Aggarwal, C. Procopiuc, J. Wolf, P. Yu, and J. Park. A framework for finding projected clusters in high dimensional spaces. In SIGMOD Conf, 1999.
    • (1999) SIGMOD Conf
    • Aggarwal, C.1    Procopiuc, C.2    Wolf, J.3    Yu, P.4    Park, J.5
  • 3
    • 0039253822 scopus 로고    scopus 로고
    • Finding generalized projected clusters in high-dimensional spaces
    • C. Aggarwal and P. Yu. Finding generalized projected clusters in high-dimensional spaces. In SIGMOD Conf, 2000.
    • (2000) SIGMOD Conf
    • Aggarwal, C.1    Yu, P.2
  • 4
    • 0032090765 scopus 로고    scopus 로고
    • Automatic subspace clustering of high dimensional data for data mining applications
    • R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD Conf, 1998.
    • (1998) SIGMOD Conf
    • Agrawal, R.1    Gehrke, J.2    Gunopulos, D.3    Raghavan, P.4
  • 5
    • 0027621699 scopus 로고
    • Mining association rules between sets ofitems in large databases
    • R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets ofitems in large databases. In SIGMOD Conf, 1993.
    • (1993) SIGMOD Conf
    • Agrawal, R.1    Imielinski, T.2    Swami, A.3
  • 6
    • 4243733322 scopus 로고    scopus 로고
    • Methods of combining multiple classifiers based on different representations for penbased handwriting recognition
    • F. Alimoglu and E. Alpaydin. Methods of combining multiple classifiers based on different representations for penbased handwriting recognition. In Turkish AI and Neural Networks Symp, 1996.
    • (1996) Turkish AI and Neural Networks Symp
    • Alimoglu, F.1    Alpaydin, E.2
  • 8
    • 0035534927 scopus 로고    scopus 로고
    • A variable selection heuristic for k-means clustering
    • M. Brusco and J. Cradit. A variable selection heuristic for k-means clustering. Psychometrika, 66:249-270, 2001.
    • (2001) Psychometrika , vol.66 , pp. 249-270
    • Brusco, M.1    Cradit, J.2
  • 9
    • 0005287692 scopus 로고    scopus 로고
    • Local dimensionality reduction: A new approach to indexing high dimensional spaces
    • K. Chakrabarti and S. Mehrotra. Local dimensionality reduction: A new approach to indexing high dimensional spaces. In VLDB Conf, 2000.
    • (2000) VLDB Conf
    • Chakrabarti, K.1    Mehrotra, S.2
  • 10
    • 0002646822 scopus 로고    scopus 로고
    • Entropy-based subspace clustering for mining numerical data
    • C. Cheng, A. Fu, Y. Zhang. Entropy-based subspace clustering for mining numerical data. In SIGKDD Conf, 1999.
    • (1999) SIGKDD Conf
    • Cheng, C.1    Fu, A.2    Zhang, Y.3
  • 11
    • 0000182415 scopus 로고
    • A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
    • H. Chernoff. A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Annals of Math. Statistics, 23:493-509, 1952.
    • (1952) Annals of Math. Statistics , vol.23 , pp. 493-509
    • Chernoff, H.1
  • 12
    • 78149305852 scopus 로고    scopus 로고
    • Adaptive dimension reduction for clustering high-dimensional data
    • C. Ding, X. He, H. Zha, and H. Simon. Adaptive dimension reduction for clustering high-dimensional data. In ICDM Conf, 2002.
    • (2002) ICDM Conf
    • Ding, C.1    He, X.2    Zha, H.3    Simon, H.4
  • 13
    • 78149351437 scopus 로고    scopus 로고
    • Efficiently mining maximal frequent itemsets
    • K. Gouda and M. Zaki. Efficiently mining maximal frequent itemsets. In ICDM Conf, 2001.
    • (2001) ICDM Conf
    • Gouda, K.1    Zaki, M.2
  • 15
    • 0000835955 scopus 로고    scopus 로고
    • Optimal grid-clustering: Towards breaking the curse of dimensionality in high-dimensional clustering
    • A. Hinneburg and D. Keim. Optimal grid-clustering: Towards breaking the curse of dimensionality in high-dimensional clustering. In VLDB Conf, 1999.
    • (1999) VLDB Conf
    • Hinneburg, A.1    Keim, D.2
  • 16
    • 84947403595 scopus 로고
    • Probability inequalities for sums of bounded random variables
    • W. Hoeffding. Probability inequalities for sums of bounded random variables. J. American Statistical Association, 58:13-30, 1963.
    • (1963) J. American Statistical Association , vol.58 , pp. 13-30
    • Hoeffding, W.1
  • 17
    • 2942588997 scopus 로고    scopus 로고
    • Density-connected subspace clustering for high-dimensional data
    • K. Kailing, H. Kriegel, and P. Kroger. Density-connected subspace clustering for high-dimensional data. In SIAM Data Mining Conf, 2004.
    • (2004) SIAM Data Mining Conf
    • Kailing, K.1    Kriegel, H.2    Kroger, P.3
  • 18
  • 21
    • 0242625291 scopus 로고    scopus 로고
    • Selecting the right interestingness measure for association patterns
    • P. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. In SIGKDD Conf, 2002.
    • (2002) SIGKDD Conf
    • Tan, P.1    Kumar, V.2    Srivastava, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.