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Volumn , Issue , 2011, Pages 733-744

Advancing data clustering via projective clustering ensembles

Author keywords

clustering; clustering ensembles; data mining; dimensionality reduction; optimization; projective clustering; subspace clustering

Indexed keywords

APPROXIMATION ALGORITHMS; CLUSTER COMPUTING; CLUSTERING ALGORITHMS; DATA MINING; HEURISTIC METHODS; OPTIMIZATION;

EID: 79959952086     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989400     Document Type: Conference Paper
Times cited : (15)

References (37)
  • 3
    • 35248893423 scopus 로고    scopus 로고
    • Finding Natural Clusters Using Multi-Clusterer Combiner Based on Shared Nearest Neighbors
    • H. Ayad and M. S. Kamel. Finding Natural Clusters Using Multi-Clusterer Combiner Based on Shared Nearest Neighbors. In Proc. Int. Workshop on Multiple Classifier Systems (MCS), pages 166-175, 2003.
    • (2003) Proc. Int. Workshop on Multiple Classifier Systems (MCS) , pp. 166-175
    • Ayad, H.1    Kamel, M.S.2
  • 5
    • 19544386608 scopus 로고    scopus 로고
    • Density Connected Clustering with Local Subspace Preferences
    • C. Böhm, K. Kailing, H. P. Kriegel, and P. Kröger. Density Connected Clustering with Local Subspace Preferences. In Proc. ICDM Conf., pages 27-34, 2004.
    • (2004) Proc. ICDM Conf. , pp. 27-34
    • Böhm, C.1    Kailing, K.2    Kriegel, H.P.3    Kröger, P.4
  • 6
    • 35048863148 scopus 로고    scopus 로고
    • Combining Multiple Clustering Systems
    • C. Boulis and M. Ostendorf. Combining Multiple Clustering Systems. In Proc. PKDD Conf., pages 63-74, 2004.
    • (2004) Proc. PKDD Conf. , pp. 63-74
    • Boulis, C.1    Ostendorf, M.2
  • 7
    • 0002550769 scopus 로고    scopus 로고
    • Refining Initial Points for K-Means Clustering
    • P. S. Bradley and U. M. Fayyad. Refining Initial Points for K-Means Clustering. In Proc. ICML Conf., pages 91-99, 1998.
    • (1998) Proc. ICML Conf. , pp. 91-99
    • Bradley, P.S.1    Fayyad, U.M.2
  • 8
    • 67049160135 scopus 로고    scopus 로고
    • A Probability Model for Projective Clustering on High Dimensional Data
    • L. Chen, Q. Jiang, and S. Wang. A Probability Model for Projective Clustering on High Dimensional Data. In Proc. ICDM Conf., pages 755-760, 2008.
    • (2008) Proc. ICDM Conf. , pp. 755-760
    • Chen, L.1    Jiang, Q.2    Wang, S.3
  • 10
    • 84958955890 scopus 로고    scopus 로고
    • Voting-Merging: An Ensemble Method for Clustering
    • E. Dimitriadou, A. Weingesse, and K. Hornik. Voting-Merging: An Ensemble Method for Clustering. In Proc. ICANN Conf., pages 217-224, 2001.
    • (2001) Proc. ICANN Conf. , pp. 217-224
    • Dimitriadou, E.1    Weingesse, A.2    Hornik, K.3
  • 11
    • 0038391443 scopus 로고    scopus 로고
    • Bagging to improve the accuracy of a clustering procedure
    • DOI 10.1093/bioinformatics/btg038
    • S. Dudoit and J. Fridlyand. Bagging to Improve the Accuracy of a Clustering Procedure. Bioinformatics, 19(9):1090-1099, 2003. (Pubitemid 36790010)
    • (2003) Bioinformatics , vol.19 , Issue.9 , pp. 1090-1099
    • Dudoit, S.1    Fridlyand, J.2
  • 12
    • 0442296539 scopus 로고    scopus 로고
    • Bagging for Path-Based Clustering
    • B. Fischer and J. M. Buhmann. Bagging for Path-Based Clustering. TPAMI, 25(11):1411-1415, 2003.
    • (2003) TPAMI , vol.25 , Issue.11 , pp. 1411-1415
    • Fischer, B.1    Buhmann, J.M.2
  • 17
    • 72849135756 scopus 로고    scopus 로고
    • Diversity-Based Weighting Schemes for Clustering Ensembles
    • F. Gullo, A. Tagarelli, and S. Greco. Diversity-Based Weighting Schemes for Clustering Ensembles. In Proc. SDM Conf., pages 437-448, 2009.
    • (2009) Proc. SDM Conf. , pp. 437-448
    • Gullo, F.1    Tagarelli, A.2    Greco, S.3
  • 19
    • 0032131147 scopus 로고    scopus 로고
    • A fast and high quality multilevel scheme for partitioning irregular graphs
    • G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comp., 20(1):359-392, 1998.
    • (1998) SIAM J. Sci. Comp. , vol.20 , Issue.1 , pp. 359-392
    • Karypis, G.1    Kumar, V.2
  • 21
    • 0000309136 scopus 로고    scopus 로고
    • Gaussian clustering method based on maximum-fuzzy-entropy interpretation
    • PII S0165011497001267
    • R. P. Li and M. Mukaidono. Gaussian clustering method based on maximum-fuzzy-entropy interpretation. Fuzzy Sets and Systems, 102(2):253-258, 1999. (Pubitemid 129510836)
    • (1999) Fuzzy Sets and Systems , vol.102 , Issue.2 , pp. 253-258
    • Li, R.-P.1    Mukaidono, M.2
  • 25
    • 19544389465 scopus 로고    scopus 로고
    • SCHISM: A New Approach for Interesting Subspace Mining
    • K. Sequeira and M. Zaki. SCHISM: A New Approach for Interesting Subspace Mining. In Proc. ICDM Conf., pages 186-193, 2004.
    • (2004) Proc. ICDM Conf. , pp. 186-193
    • Sequeira, K.1    Zaki, M.2
  • 28
    • 0041965980 scopus 로고    scopus 로고
    • Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions
    • A. Strehl and J. Ghosh. Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. J. Mach. Learn. Res., 3:583-617, 2002.
    • (2002) J. Mach. Learn. Res. , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 29
    • 67549123292 scopus 로고    scopus 로고
    • Weighted Cluster Ensembles: Methods and Analysis
    • C. Domeniconi and M. Al-Razgan. Weighted Cluster Ensembles: Methods and Analysis. TKDD, 2(4), 2009.
    • (2009) TKDD , vol.2 , Issue.4
    • Domeniconi, C.1    Al-Razgan, M.2
  • 32
    • 14644424597 scopus 로고    scopus 로고
    • Projective Clustering by Histograms
    • E. Ka Ka Ng, A. W.-C. Fu, and R. C.-W. Wong. Projective Clustering by Histograms. TKDE, 17(3):369-383, 2005.
    • (2005) TKDE , vol.17 , Issue.3 , pp. 369-383
    • Ka Ka Ng, E.1    Fu, A.W.-C.2    Wong, R.C.-W.3
  • 34
    • 41149085604 scopus 로고    scopus 로고
    • Robust projected clustering
    • G. Moise, J. Sander, and M. Ester. Robust projected clustering. KAIS, 14(3):273-298, 2008.
    • (2008) KAIS , vol.14 , Issue.3 , pp. 273-298
    • Moise, G.1    Sander, J.2    Ester, M.3
  • 36
    • 14344258244 scopus 로고    scopus 로고
    • Solving Cluster Ensemble Problems by Bipartite Graph Partitioning
    • X. Z. Fern and C. Brodley. Solving Cluster Ensemble Problems by Bipartite Graph Partitioning. In Proc. ICML Conf., pages 281-288, 2004.
    • (2004) Proc. ICML Conf. , pp. 281-288
    • Fern, X.Z.1    Brodley, C.2
  • 37
    • 28444491389 scopus 로고    scopus 로고
    • On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering
    • K. Y. Yip, D. W. Cheung, and M. K. Ng. On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering. In Proc. ICDE Conf., pages 329-340, 2005.
    • (2005) Proc. ICDE Conf. , pp. 329-340
    • Yip, K.Y.1    Cheung, D.W.2    Ng, M.K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.