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Volumn 772, Issue , 2011, Pages 7-18

When pattern met subspace cluster: A relationship story

Author keywords

[No Author keywords available]

Indexed keywords

CURRENT PROBLEMS; PATTERN MINING; SUB-SPACE CLUSTERING; SUBSPACE CLUSTERS;

EID: 84891061607     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (51)
  • 2
    • 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 Proc. SIGMOD, 1998.
    • (1998) Proc. SIGMOD
    • Agrawal, R.1    Gehrke, J.2    Gunopulos, D.3    Raghavan, P.4
  • 3
    • 9444260615 scopus 로고
    • Fast algorithms for mining association rules
    • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. SIGMOD, 1994.
    • (1994) Proc. SIGMOD
    • Agrawal, R.1    Srikant, R.2
  • 6
    • 84873117260 scopus 로고    scopus 로고
    • COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity
    • E. Bae and J. Bailey. COALA: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In Proc. ICDM, 2006.
    • (2006) Proc. ICDM
    • Bae, E.1    Bailey, J.2
  • 7
    • 0032091573 scopus 로고    scopus 로고
    • Efficiently mining long patterns from databases
    • R. Bayardo. Efficiently mining long patterns from databases. In Proc. SIGMOD, pages 85-93, 1998.
    • (1998) Proc. SIGMOD , pp. 85-93
    • Bayardo, R.1
  • 8
    • 0012908433 scopus 로고    scopus 로고
    • Density-based indexing for approximate nearest-neighbor queries
    • K. P. Bennett, U. Fayyad, and D. Geiger. Density-based indexing for approximate nearest-neighbor queries. In Proc. KDD, 1999.
    • (1999) Proc. KDD
    • Bennett, K.P.1    Fayyad, U.2    Geiger, D.3
  • 13
    • 33847406228 scopus 로고    scopus 로고
    • Non-derivable itemset mining
    • T. Calders and B. Goethals. Non-derivable itemset mining. Data Min. Knowl. Disc., 14(1):171-206, 2007.
    • (2007) Data Min. Knowl. Disc. , vol.14 , Issue.1 , pp. 171-206
    • Calders, T.1    Goethals, B.2
  • 14
    • 0002646822 scopus 로고    scopus 로고
    • Entropy-based subspace clustering for mining numerical data
    • C. H. Cheng, A. W.-C. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In Proc. KDD, pages 84-93, 1999.
    • (1999) Proc. KDD , pp. 84-93
    • Cheng, C.H.1    Fu, A.W.-C.2    Zhang, Y.3
  • 15
    • 84873124076 scopus 로고    scopus 로고
    • Generation of alternative clusterings using the CAMI approach
    • X. H. Dang and J. Bailey. Generation of alternative clusterings using the CAMI approach. In Proc. SDM, 2010.
    • (2010) Proc. SDM
    • Dang, X.H.1    Bailey, J.2
  • 16
    • 84873178417 scopus 로고    scopus 로고
    • A SAT-based framework for efficient constrained clustering
    • I. Davidson, S. S. Ravi, and L. Shamis. A SAT-based framework for efficient constrained clustering. In Proc. SDM, 2010.
    • (2010) Proc. SDM
    • Davidson, I.1    Ravi, S.S.2    Shamis, L.3
  • 17
    • 80855129853 scopus 로고    scopus 로고
    • Maximum entropy models and subjective interestingness: An application to tiles in binary databases
    • T. De Bie. Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Disc., 2010.
    • (2010) Data Min. Knowl. Disc.
    • De Bie, T.1
  • 18
    • 0000550189 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. KDD, 1996.
    • (1996) Proc. KDD
    • Ester, M.1    Kriegel, H.-P.2    Sander, J.3    Xu, X.4
  • 20
    • 34249788454 scopus 로고    scopus 로고
    • The concentration of fractional distances
    • D. Francois, V.Wertz, and M. Verleysen. The concentration of fractional distances. IEEE TKDE, 19(7):873-886, 2007.
    • (2007) IEEE TKDE , vol.19 , Issue.7 , pp. 873-886
    • Francois, D.1    Wertz, V.2    Verleysen, M.3
  • 24
    • 19544376987 scopus 로고    scopus 로고
    • Non-redundant data clustering
    • D. Gondek and T. Hofmann. Non-redundant data clustering. In Proc. ICDM, 2004.
    • (2004) Proc. ICDM
    • Gondek, D.1    Hofmann, T.2
  • 27
    • 74549217295 scopus 로고    scopus 로고
    • Detection of orthogonal concepts in subspaces of high dimensional data
    • S. Günnemann, E. Müller, I. Färber, and T. Seidl. Detection of orthogonal concepts in subspaces of high dimensional data. In Proc. CIKM, 2009.
    • (2009) Proc. CIKM
    • Günnemann, S.1    Müller, E.2    Färber, I.3    Seidl, T.4
  • 28
    • 70350663120 scopus 로고    scopus 로고
    • Tell me something i don't know: Randomization strategies for iterative data mining
    • S. Hanhijärvi, M. Ojala, N. Vuokko, K. Puolamäki, N. Tatti, and H. Mannila. Tell me something I don't know: randomization strategies for iterative data mining. In Proc. KDD, pages 379-388, 2009.
    • (2009) Proc. KDD , pp. 379-388
    • Hanhijärvi, S.1    Ojala, M.2    Vuokko, N.3    Puolamäki, K.4    Tatti, N.5    Mannila, H.6
  • 30
    • 70049108502 scopus 로고    scopus 로고
    • Simultaneous unsupervised learning of disparate clusterings
    • P. Jain, R. Meka, and I. S. Dhillon. Simultaneous unsupervised learning of disparate clusterings. Stat. Anal. Data Min., 1(3):195-210, 2008.
    • (2008) Stat. Anal. Data Min. , vol.1 , Issue.3 , pp. 195-210
    • Jain, P.1    Meka, R.2    Dhillon, I.S.3
  • 31
    • 33750297146 scopus 로고    scopus 로고
    • Density-connected subspace clustering for high-dimensional data
    • K. Kailing, H.-P. Kriegel, and P. Kröger. Density-connected subspace clustering for high-dimensional data. In Proc. SDM, 2004.
    • (2004) Proc. SDM
    • Kailing, K.1    Kriegel, H.-P.2    Kröger, P.3
  • 33
    • 77956254622 scopus 로고    scopus 로고
    • An information-theoretic approach to finding informative noisy tiles in binary databases
    • K.-N. Kontonasios and T. DeBie. An information-theoretic approach to finding informative noisy tiles in binary databases. In Proc. SDM, 2010.
    • (2010) Proc. SDM
    • Kontonasios, K.-N.1    Debie, T.2
  • 34
    • 67149084291 scopus 로고    scopus 로고
    • Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering
    • H.-P. Kriegel, P. Kröger, and A. Zimek. Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM TKDD, 3(1):1-58, 2009.
    • (2009) ACM TKDD , vol.3 , Issue.1 , pp. 1-58
    • Kriegel, H.-P.1    Kröger, P.2    Zimek, A.3
  • 36
    • 84891112684 scopus 로고    scopus 로고
    • Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: What can we learn from each other?
    • H.-P. Kriegel and A. Zimek. Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: What can we learn from each other? In Proc. ACM SIGKDD Workshop MultiClust, 2010.
    • (2010) Proc ACM SIGKDD Workshop MultiClust
    • Kriegel, H.-P.1    Zimek, A.2
  • 37
    • 34548723854 scopus 로고    scopus 로고
    • Distance based subspace clustering with flexible dimension partitioning
    • G. Liu, J. Li, K. Sim, and L. Wong. Distance based subspace clustering with flexible dimension partitioning. In Proc. ICDE, 2007.
    • (2007) Proc. ICDE
    • Liu, G.1    Li, J.2    Sim, K.3    Wong, L.4
  • 38
    • 0002056465 scopus 로고
    • Version spaces: A candidate elimination approach to rule learning
    • T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. In Proc. IJCAI, 1977.
    • (1977) Proc. IJCAI
    • Mitchell, T.M.1
  • 39
    • 84857180195 scopus 로고    scopus 로고
    • Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression
    • F. Moerchen, M. Thies, and A. Ultsch. Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression. KAIS, 2010.
    • (2010) KAIS
    • Moerchen, F.1    Thies, M.2    Ultsch, A.3
  • 40
    • 65449163900 scopus 로고    scopus 로고
    • Finding non-redundant, statistically significant regions in high dimensional data: A novel approach to projected and subspace clustering
    • G. Moise and J. Sander. Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering. In Proc. KDD, 2008.
    • (2008) Proc. KDD
    • Moise, G.1    Sander, J.2
  • 41
    • 77951149821 scopus 로고    scopus 로고
    • Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data
    • E. Müller, I. Assent, S. Günnemann, R. Krieger, and T. Seidl. Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data. In Proc. ICDM, 2009.
    • (2009) Proc. ICDM
    • Müller, E.1    Assent, I.2    Günnemann, S.3    Krieger, R.4    Seidl, T.5
  • 43
    • 41149123106 scopus 로고    scopus 로고
    • Adaptive grids for clustering massive data sets
    • H.S. Nagesh, S. Goil, and A. Choudhary. Adaptive grids for clustering massive data sets. In Proc. SDM, 2001.
    • (2001) Proc. SDM
    • Nagesh, H.S.1    Goil, S.2    Choudhary, A.3
  • 44
    • 79951736794 scopus 로고    scopus 로고
    • Assessing data mining results on matrices with randomization
    • M. Ojala. Assessing data mining results on matrices with randomization. In Proc. ICDM, 2010.
    • (2010) Proc. ICDM
    • Ojala, M.1
  • 45
    • 77950233355 scopus 로고    scopus 로고
    • Randomization methods for assessing data analysis results on real-valued matrices
    • M. Ojala, N. Vuokko, A. Kallio, N. Haiminen, and H. Mannila. Randomization methods for assessing data analysis results on real-valued matrices. Stat. Anal. Data Min., 2(4):209-230, 2009.
    • (2009) Stat. Anal. Data Min. , vol.2 , Issue.4 , pp. 209-230
    • Ojala, M.1    Vuokko, N.2    Kallio, A.3    Haiminen, N.4    Mannila, H.5
  • 46
  • 47
    • 70350663111 scopus 로고    scopus 로고
    • A principled and flexible framework for finding alternative clusterings
    • Z. J. Qi and I. Davidson. A principled and flexible framework for finding alternative clusterings. In Proc. KDD, 2009.
    • (2009) Proc. KDD
    • Qi, Z.J.1    Davidson, I.2
  • 50
    • 34249653461 scopus 로고    scopus 로고
    • Discovering significant patterns
    • G. I. Webb. Discovering significant patterns. Mach. Learn., 68(1):1-33, 2007.
    • (2007) Mach. Learn. , vol.68 , Issue.1 , pp. 1-33
    • Webb, G.I.1
  • 51
    • 32344439809 scopus 로고    scopus 로고
    • Summarizing itemset patterns: A profilebased approach
    • X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset patterns: a profilebased approach. In Proc. KDD, 2005.
    • (2005) Proc. KDD
    • Yan, X.1    Cheng, H.2    Han, J.3    Xin, D.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.