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Volumn 43, Issue 9, 2010, Pages 3130-3143

On cluster tree for nested and multi-density data clustering

Author keywords

Cluster tree; Hierarchical clustering; K Means type algorithm; Multi densities

Indexed keywords

DATA MINING; TREES (MATHEMATICS);

EID: 78649574534     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.03.020     Document Type: Article
Times cited : (26)

References (26)
  • 3
    • 0032686723 scopus 로고    scopus 로고
    • CHAMELEON: A hierarchical clustering algorithm using dynamic modeling
    • G. Karypis, J. Han, V. Kumar, CHAMELEON: a hierarchical clustering algorithm using dynamic modeling, IEEE Computer 32 (8) (1999) 68-75.
    • (1999) IEEE Computer , vol.32 , Issue.8 , pp. 68-75
    • Karypis, G.1    Han, J.2    Kumar, V.3
  • 8
    • 34347333289 scopus 로고    scopus 로고
    • A local-density based spatial clustering algorithm with noise
    • DOI 10.1016/j.is.2006.10.006, PII S0306437906000871
    • L. Duan, L. Xu, F. Guo, J. Lee, B. Yan, A local-density based spatial clustering algorithm with noise, Information Systems 32 (2007) 978-986. (Pubitemid 47017576)
    • (2007) Information Systems , vol.32 , Issue.7 , pp. 978-986
    • Duan, L.1    Xu, L.2    Guo, F.3    Lee, J.4    Yan, B.5
  • 9
    • 34547519334 scopus 로고    scopus 로고
    • A grid-based density-confidence-interval clustering algorithm for multi-density dataset in large spatial database
    • S. Gao, Y. Xia, A grid-based density-confidence-interval clustering algorithm for multi-density dataset in large spatial database, in: International Conference on Intelligent Systems Design and Applications, vol. 1, 2006, pp. 713-717.
    • (2006) International Conference on Intelligent Systems Design and Applications , vol.1 , pp. 713-717
    • Gao, S.1    Xia, Y.2
  • 12
    • 52949101047 scopus 로고    scopus 로고
    • Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters
    • M. Li, M. K. Ng, Y. M. Cheung, Z. Huang, Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters, IEEE Transaction on Knowledge and Engineering 20 (11) (2008) 1519-1534.
    • (2008) IEEE Transaction on Knowledge and Engineering , vol.20 , Issue.11 , pp. 1519-1534
    • Li, M.1    Ng, M.K.2    Cheung, Y.M.3    Huang, Z.4
  • 13
    • 4544367326 scopus 로고    scopus 로고
    • FCM-based model selection algorithms for determining the number of clusters
    • H. Sun, S. Wang, Q. Jiang, FCM-based model selection algorithms for determining the number of clusters, Pattern Recognition 37 (2004) 2027-2037.
    • (2004) Pattern Recognition , vol.37 , pp. 2027-2037
    • Sun, H.1    Wang, S.2    Jiang, Q.3
  • 15
    • 84885969802 scopus 로고
    • Omega: A general formulation of the rand index of cluster recovery suitable for non-disjoint solutions
    • L. M. Collins, C. W. Dent, Omega: a general formulation of the rand index of cluster recovery suitable for non-disjoint solutions, Multivariate Behavioral Research 23 (1988) 231-242.
    • (1988) Multivariate Behavioral Research , vol.23 , pp. 231-242
    • Collins, L.M.1    Dent, C.W.2
  • 19
    • 0032091595 scopus 로고    scopus 로고
    • CURE: An efficient clustering algorithm for large databases
    • S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large database, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, 1998, pp. 73-84. (Pubitemid 128655958)
    • (1998) SIGMOD Record , vol.27 , Issue.2 , pp. 73-84
    • Guha, S.1    Rastogi, R.2    Shim, K.3
  • 21
    • 10844255636 scopus 로고    scopus 로고
    • The evolving tree - A novel self-organizing network for data analysis
    • DOI 10.1007/s11063-004-2156-8
    • J. Pakkanen, J. Iivarinen, E. Oja, The evolving tree-a novel self-organizing network for data analysis, Neural Processing Letters 20 (2004) 199-211. (Pubitemid 40003525)
    • (2004) Neural Processing Letters , vol.20 , Issue.3 , pp. 199-211
    • Pakkanen, J.1    Iivarinen, J.2    Oja, E.3
  • 23
    • 14344262272 scopus 로고    scopus 로고
    • Automated hierarchical mixtures of probabilistic principal component analyzers
    • Banff, Canada, July
    • T. Su, J. Dy, Automated hierarchical mixtures of probabilistic principal component analyzers, in: Proceedings of the 21st International Conference on Machine Learning, no. 98 Banff, Canada, July 2004.
    • (2004) Proceedings of the 21st International Conference on Machine Learning , vol.98
    • Su, T.1    Dy, J.2
  • 24
    • 33746597003 scopus 로고    scopus 로고
    • Large-scale data exploration with the hierarchically growing hyperbolic SOM
    • DOI 10.1016/j.neunet.2006.05.015, PII S0893608006000797
    • J. Ontrup, H. Ritter, Large scale data exploration with the hierarchical growing hyperbolic SOM, Neural Networks 19 (2006) 751-761. (Pubitemid 44148953)
    • (2006) Neural Networks , vol.19 , Issue.6-7 , pp. 751-761
    • Ontrup, J.1    Ritter, H.2
  • 26
    • 47049115023 scopus 로고    scopus 로고
    • Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification
    • DOI 10.1093/bioinformatics/btn257
    • C. Martin, N. Diaz, J. Ontrup, T. Nattkemper, Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification, Bioinformatics 24 (2008) 1568-1574. (Pubitemid 351966154)
    • (2008) Bioinformatics , vol.24 , Issue.14 , pp. 1568-1574
    • Martin, C.1    Diaz, N.N.2    Ontrup, J.3    Nattkemper, T.W.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.