메뉴 건너뛰기




Volumn 2, Issue 5-6, 2009, Pages 427-444

Efficient mining of distance-based subspace clusters

Author keywords

Biclustering; Distance based clustering; Subspace clustering

Indexed keywords

GENE EXPRESSION;

EID: 77950250511     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10062     Document Type: Article
Times cited : (13)

References (42)
  • 3
    • 84949479246 scopus 로고    scopus 로고
    • On the surprising behavior of distance metrics in high dimensional spaces
    • London UK
    • C. C. Aggarwal, A. Hinneburg, and D. A. Keim, On the surprising behavior of distance metrics in high dimensional spaces, In Proceedings of the 8th ICDT Conference, London, UK, 2001, 420-434.
    • (2001) Proceedings of the 8th ICDT Conference , pp. 420-434
    • Aggarwal, C.C.1    Hinneburg, A.2    Keim, D.A.3
  • 4
    • 17044376078 scopus 로고    scopus 로고
    • Subspace clustering for high dimensional data: a review
    • Lance Parsons, Ehtesham Haque, and Huan Liu, Subspace clustering for high dimensional data: a review. SIGKDD Explor Newslett 6(1) (2004), 90-105.
    • (2004) SIGKDD Explor Newslett , vol.6 , Issue.1 , pp. 90-105
    • Parsons, L.1    Haque, E.2    Liu, H.3
  • 5
    • 67149084291 scopus 로고    scopus 로고
    • Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering
    • Hans-Peter Kriegel, Peer Kr̈oger, and Arthur Zimek, Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Dis Data Min 3(1) (2009), 1-58.
    • (2009) ACM Trans Knowl Dis Data Min , vol.3 , Issue.1 , pp. 1-58
    • Kriegel, H.P.1    Kröger P2    Zimek A3
  • 7
    • 0002646822 scopus 로고    scopus 로고
    • Entropybased subspace clustering for mining numerical data
    • San Diego, California USA
    • C. H. Cheng, A. W.-C. Fu, and Yi Zhang, Entropybased subspace clustering for mining numerical data, In Proceedings of the 5th ACM SIGKDD Conference, San Diego, California, USA, 1999, 84-93.
    • (1999) Proceedings of the 5th ACM SIGKDD Conference , pp. 84-93
    • Cheng, C.H.1    Fu, A.W.C.2    Zhang, Yi.3
  • 9
    • 0036039291 scopus 로고    scopus 로고
    • A new cell-based clustering method for large, high-dimensional data in data mining applications
    • Madrid Spain
    • J.-W. Chang and D.-S. Jin, A new cell-based clustering method for large, high-dimensional data in data mining applications, In Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain, 2002, 503-507.
    • (2002) Proceedings of the 2002 ACM Symposium on Applied Computing , pp. 503-507
    • Chang, J.W.1    Jin, D.S.2
  • 10
    • 85049067327 scopus 로고    scopus 로고
    • Clustering through decision tree construction
    • McLean, VA USA
    • B. Liu, Y. Xia, and P. S. Yu, Clustering through decision tree construction, In Proceedings of the 9th CIKM Conference, McLean, VA, USA, 2000, 20-29.
    • (2000) Proceedings of the 9th CIKM Conference , pp. 20-29
    • Liu, B.1    Xia, Y.2    Yu, P.S.3
  • 11
    • 34548723854 scopus 로고    scopus 로고
    • Distance based subspace clustering with flexible dimension partitioning
    • Istanbul Turkey
    • G. Liu, J. Li, K. Sim, and L. Wong, Distance based subspace clustering with flexible dimension partitioning, In Proceedings of the 23rd ICDE Conference, Istanbul, Turkey, 2007, 1250-1254.
    • (2007) Proceedings of the 23rd ICDE Conference , pp. 1250-1254
    • Liu, G.1    Li, J.2    Sim, K.3    Wong, L.4
  • 15
    • 77953564323 scopus 로고    scopus 로고
    • collaboration of array, bitmap and prefix tree for frequent itemset mining
    • Chicago, Illinois USA
    • T. Uno, M. Kiyomi, and H. Arimura, Lcm ver. 3: collaboration of array, bitmap and prefix tree for frequent itemset mining, In Proceedings of the ACM SIGKDD OSDM Workshop, Chicago, Illinois, USA, 2005.
    • (2005) Proceedings of the ACM SIGKDD OSDM Workshop , vol.3
    • Uno, T.1    Kiyomi, M.2    Arimura, H.3
  • 16
    • 0039253846 scopus 로고    scopus 로고
    • Mining frequent patterns without candidate generation
    • Dallas, Texas USA
    • J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, In Proceedings of the 2000 ACM SIGMOD Conference, Dallas, Texas, USA, 2000, 1-12.
    • (2000) Proceedings of the 2000 ACM SIGMOD Conference , pp. 1-12
    • Han, J.1    Pei, J.2    Yin, Y.3
  • 19
    • 14644404956 scopus 로고    scopus 로고
    • Iterative projected clustering by subspace mining
    • M. L. Yiu and N. Mamoulis, Iterative projected clustering by subspace mining, IEEE Trans Knowl Data Eng, 17(2) (2005), 176-189.
    • (2005) IEEE Trans Knowl Data Eng , vol.17 , Issue.2 , pp. 176-189
    • Yiu, M.L.1    Mamoulis, N.2
  • 20
    • 65449163900 scopus 로고    scopus 로고
    • Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering
    • Las Vegas, Nevada USA
    • G. Moise and J. Sander, Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering, In Proceedings of the 14th ACM SIGKDD Conference, Las Vegas, Nevada, USA, 2008, 533-541.
    • (2008) Proceedings of the 14th ACM SIGKDD Conference , pp. 533-541
    • Moise, G.1    Sander, J.2
  • 21
    • 12344269924 scopus 로고    scopus 로고
    • Go: termfinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes
    • E. I. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein, J. M. Cherry, and G. Sherlock, Go: termfinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes, Bioinformatics 20(18) (2004), 3710-3715.
    • (2004) Bioinformatics , vol.20 , Issue.18 , pp. 3710-3715
    • Boyle, E.I.1    Weng, S.2    Gollub, J.3    Jin, H.4    Botstein, D.5    Cherry, J.M.6    Sherlock, G.7
  • 23
    • 0040154165 scopus 로고    scopus 로고
    • Re-designing distance functions and distance-based applications for high dimensional data
    • C. C. Aggarwal, Re-designing distance functions and distance-based applications for high dimensional data, SIGMOD Record 30(1) (2001), 13-18.
    • (2001) SIGMOD Record , vol.30 , Issue.1 , pp. 13-18
    • Aggarwal, C.C.1
  • 24
    • 3142768191 scopus 로고    scopus 로고
    • Biclustering algorithms for biological data analysis: a survey
    • S. C. Madeira and A. L. Oliveira, Biclustering algorithms for biological data analysis: a survey, IEE/ACM Trans Comput Biol Bioinform 01(1) (2004), 24-45.
    • (2004) IEE/ACM Trans Comput Biol Bioinform , vol.1 , Issue.1 , pp. 24-45
    • Madeira, S.C.1    Oliveira, A.L.2
  • 26
    • 85134733586 scopus 로고    scopus 로고
    • Finding generalized projected clusters in high dimensional spaces
    • Dallas, Texas USA
    • C. C. Aggarwal and P. S. Yu, Finding generalized projected clusters in high dimensional spaces, In Proceedings of the 2000 ACM SIGMOD Conference, Dallas, Texas, USA, 2000, 70-81.
    • (2000) Proceedings of the 2000 ACM SIGMOD Conference , pp. 70-81
    • Aggarwal, C.C.1    Yu, P.S.2
  • 27
    • 0742324835 scopus 로고    scopus 로고
    • Findit: a fast and intelligent subspace clustering algorithm using dimension voting
    • K.-Gu Woo, J.-H. Lee, M.-H. Kim, and Y.-J. Lee, Findit: a fast and intelligent subspace clustering algorithm using dimension voting, Inf Soft Tech, 46(4) (2004), 255-271.
    • (2004) Inf Soft Tech , vol.46 , Issue.4 , pp. 255-271
    • Gu Woo, K.1    Lee, J.-H.2    Kim, M.-H.3    Lee, Y.-J.4
  • 30
    • 0036211103 scopus 로고    scopus 로고
    • δ-clusters: capturing subspace correlation in a large data set
    • San Jose, California USA
    • J. Yang, W. Wang, H. Wang, and P. S. Yu, δ-clusters: capturing subspace correlation in a large data set, In Proceedings of the 18th IEEE ICDE Conference, San Jose, California, USA, 2002, 517-528.
    • (2002) Proceedings of the 18th IEEE ICDE Conference , pp. 517-528
    • Yang, J.1    Wang, W.2    Wang, H.3    Yu, P.S.4
  • 31
    • 5444223340 scopus 로고    scopus 로고
    • MaPle: a fast algorithm for maximal pattern-based clustering
    • Melbourne, Florida USA
    • J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu, MaPle: a fast algorithm for maximal pattern-based clustering, In Proceedings of the 3rd ICDM Conference, Melbourne, Florida, USA, 2003, 259-266.
    • (2003) Proceedings of the 3rd ICDM Conference , pp. 259-266
    • Pei, J.1    Zhang, X.2    Cho, M.3    Wang, H.4    Yu, P.S.5
  • 32
    • 33749620002 scopus 로고    scopus 로고
    • Mining shiftingand-scaling co-regulation patterns on gene expression profiles
    • Atlanta, Georgia USA
    • X. Xu, Y. Lu, A. K. H. Tung, and W. Wang, Mining shiftingand-scaling co-regulation patterns on gene expression profiles, In Proceedings of the 22nd ICDE Conference, Atlanta, Georgia, USA, 2006.
    • (2006) Proceedings of the 22nd ICDE Conference
    • Xu, X.1    Lu, Y.2    Tung, A.K.H.3    Wang, W.4
  • 33
    • 85170282443 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • Portland, Oregon USA
    • M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, In Proceedings of the 2nd ACM SIGKDD Conference, Portland, Oregon, USA, 1996, 226-231.
    • (1996) Proceedings of the 2nd ACM SIGKDD Conference , pp. 226-231
    • Ester, M.1    Kriegel, H.-P.2    Sander, J.3    Xu, X.4
  • 38
    • 13844297591 scopus 로고    scopus 로고
    • Harp: a practical projected clustering algorithm
    • K. Y. Yip, D. W. Cheung, and M. K. Ng, Harp: a practical projected clustering algorithm, IEEE Trans Knowl Data Eng, 16(11) (2004), 1387-1397.
    • (2004) IEEE Trans Knowl Data Eng , vol.16 , Issue.11 , pp. 1387-1397
    • Yip, K.Y.1    Cheung, D.W.2    Ng, M.K.3
  • 39
    • 28444491389 scopus 로고    scopus 로고
    • On discovery of extremely low-dimensional clusters using semi-supervised projected clustering
    • Tokyo Japan
    • K. Y. Yip, D. W. Cheung, and M. K. Ng, On discovery of extremely low-dimensional clusters using semi-supervised projected clustering, In Proceedings of the 21st ICDE Conference, Tokyo, Japan, 2005, 329-340.
    • (2005) Proceedings of the 21st ICDE Conference , pp. 329-340
    • Yip, K.Y.1    Cheung, D.W.2    Ng, M.K.3
  • 41


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.