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Volumn , Issue , 2004, Pages 623-628

A framework for ontology-driven subspace clustering

Author keywords

Ontology; Subspace clustering; Tendency Preserving

Indexed keywords

ALGORITHMS; DATA MINING; GENES; GENETIC ENGINEERING; HIERARCHICAL SYSTEMS; INFORMATION ANALYSIS; LEARNING SYSTEMS; MATRIX ALGEBRA; SET THEORY; YEAST;

EID: 12244273960     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1014130     Document Type: Conference Paper
Times cited : (24)

References (16)
  • 2
    • 0039253822 scopus 로고    scopus 로고
    • Finding generalized projected clusters in high dimensional spaces
    • C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.
    • (2000) SIGMOD , pp. 70-81
    • Aggarwal, C.C.1    Yu, P.S.2
  • 3
    • 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, 1998.
    • (1998) SIGMOD
    • Agrawal, R.1    Gehrke, J.2    Gunopulos, D.3    Raghavan, P.4
  • 4
    • 0036375743 scopus 로고    scopus 로고
    • Discovering local structure in gene expression data: The order-preserving submatrix problem
    • A. Ben-Dor, B. Chor, R.Karp, and Z.Yakhini. Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem. In RECOMB 2002.
    • (2002) RECOMB
    • Ben-Dor, A.1    Chor, B.2    Karp, R.3    Yakhini, Z.4
  • 6
    • 0038323536 scopus 로고    scopus 로고
    • Entropy-based subspace clustering for mining numerical data
    • C. H. Cheng, A. W. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In SIGKDD, pages 84-93, 1999.
    • (1999) SIGKDD , pp. 84-93
    • Cheng, C.H.1    Fu, A.W.2    Zhang, Y.3
  • 8
    • 0035789644 scopus 로고    scopus 로고
    • Co-clustering documents and words using bipartite spectral graph partitioning
    • I. S. Dhillon, Co-Clustering Documents and Words Using Bipartite Spectral Graph Partitioning. In SIGKDD, 2001.
    • (2001) SIGKDD
    • Dhillon, I.S.1
  • 9
    • 85170282443 scopus 로고    scopus 로고
    • A density-bsed algorithm for discovering clusters in large spatial databases with noise
    • M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-bsed algorithm for discovering clusters in large spatial databases with noise. In SIGKDD, pages 226-231, 1996.
    • (1996) SIGKDD , pp. 226-231
    • Ester, M.1    Kriegel, H.2    Sander, J.3    Xu, X.4
  • 11
    • 0002928691 scopus 로고    scopus 로고
    • Semantic compression and pattern extraction with fasicicles
    • H.V.Jagadish, J.Madar, and R. Ng. Semantic compression and pattern extraction with fasicicles. In VLDB, pages 186-196, 1999.
    • (1999) VLDB , pp. 186-196
    • Jagadish, H.V.1    Madar, J.2    Ng, R.3
  • 12
    • 12244297576 scopus 로고    scopus 로고
    • Flexible clustering by tendency in high dimensional spaces
    • Computer Science Department, UNC-CH
    • J.Liu and W.Wang. Flexible clustering by tendency in high dimensional spaces. Technical Report TR03-009, Computer Science Department, UNC-CH, 2003.
    • (2003) Technical Report , vol.TR03-009
    • Liu, J.1    Wang, W.2
  • 16
    • 0036372484 scopus 로고    scopus 로고
    • Clustering by pattern similarity in large, data sets
    • H. Wang, W. Wang, J. Yang, and P. Yu. Clustering by pattern similarity in large, data sets, in SIGMOD, pp. 394-405, 2002.
    • (2002) SIGMOD , pp. 394-405
    • Wang, H.1    Wang, W.2    Yang, J.3    Yu, P.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.