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Volumn 69, Issue 2, 2010, Pages 211-228

Collaborative clustering with background knowledge

Author keywords

Classification; Collaborative clustering; Knowledge guided clustering; Pattern recognition; Unsupervised learning

Indexed keywords

BACKGROUND KNOWLEDGE; CLASSIFICATION; CLUSTERING METHODS; COLLABORATION PROCESS; COLLABORATIVE CLUSTERING; COLLABORATIVE PROCESS; DATA SETS; HARD TASK;

EID: 73049108426     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.10.004     Document Type: Article
Times cited : (75)

References (57)
  • 3
    • 73049091496 scopus 로고    scopus 로고
    • P. Berkhin. Survey of clustering data mining techniques, Technical Report, Accrue Software, San Jose, CA, 2002.
    • P. Berkhin. Survey of clustering data mining techniques, Technical Report, Accrue Software, San Jose, CA, 2002.
  • 8
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles - a knowledge reuse framework for combining multiple partitions
    • Strehl A., and Ghosh J. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research 3 (2002) 583-617
    • (2002) Journal on Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 13
  • 15
    • 34548569516 scopus 로고    scopus 로고
    • Combining multiple clusterings by soft correspondence
    • IEEE Computer Society
    • Long B., Zhang Z., and Yu P.S. Combining multiple clusterings by soft correspondence. International Conference on Data Mining (2005), IEEE Computer Society 282-289
    • (2005) International Conference on Data Mining , pp. 282-289
    • Long, B.1    Zhang, Z.2    Yu, P.S.3
  • 16
    • 33745957765 scopus 로고    scopus 로고
    • Maximum likelihood combination of multiple clusterings
    • Hu T., Yu Y., Xiong J., and Sung S.Y. Maximum likelihood combination of multiple clusterings. Pattern Recognition Letters 27 13 (2006) 1457-1464
    • (2006) Pattern Recognition Letters , vol.27 , Issue.13 , pp. 1457-1464
    • Hu, T.1    Yu, Y.2    Xiong, J.3    Sung, S.Y.4
  • 17
    • 43249086150 scopus 로고    scopus 로고
    • A consensus-driven fuzzy clustering
    • Pedrycz W., and Hirota K. A consensus-driven fuzzy clustering. Pattern Recognition Letters 29 9 (2008) 1333-1343
    • (2008) Pattern Recognition Letters , vol.29 , Issue.9 , pp. 1333-1343
    • Pedrycz, W.1    Hirota, K.2
  • 19
    • 33745727034 scopus 로고    scopus 로고
    • Multi-objective optimization using genetic algorithms: a tutorial
    • Konak A., Coit D., and Smith A. Multi-objective optimization using genetic algorithms: a tutorial. Reliability Engineering & System Safety 91 9 (2006) 992-1007
    • (2006) Reliability Engineering & System Safety , vol.91 , Issue.9 , pp. 992-1007
    • Konak, A.1    Coit, D.2    Smith, A.3
  • 25
    • 0036885192 scopus 로고    scopus 로고
    • Collaborative fuzzy clustering
    • Pedrycz W. Collaborative fuzzy clustering. Pattern Recognition Letters 23 (2002) 1675-1686
    • (2002) Pattern Recognition Letters , vol.23 , pp. 1675-1686
    • Pedrycz, W.1
  • 27
    • 37249013395 scopus 로고    scopus 로고
    • Semantic web content analysis: a study in proximity-based collaborative clustering
    • Loia V., Pedrycz W., and Senatore S. Semantic web content analysis: a study in proximity-based collaborative clustering. IEEE Transactions on Fuzzy Systems 15 6 (2007) 1294-1312
    • (2007) IEEE Transactions on Fuzzy Systems , vol.15 , Issue.6 , pp. 1294-1312
    • Loia, V.1    Pedrycz, W.2    Senatore, S.3
  • 29
    • 2342557114 scopus 로고    scopus 로고
    • Clustering classifiers for knowledge discovery from physically distributed databases
    • Tsoumakas G., Angelis L., and Vlahavas I. Clustering classifiers for knowledge discovery from physically distributed databases. Data & Knowledge Engineering 49 3 (2004) 223-242
    • (2004) Data & Knowledge Engineering , vol.49 , Issue.3 , pp. 223-242
    • Tsoumakas, G.1    Angelis, L.2    Vlahavas, I.3
  • 32
    • 56249141770 scopus 로고    scopus 로고
    • An active learning framework for semi-supervised document clustering with language modeling
    • Huang R., and Lam W. An active learning framework for semi-supervised document clustering with language modeling. Data & Knowledge Engineering 68 1 (2009) 49-67
    • (2009) Data & Knowledge Engineering , vol.68 , Issue.1 , pp. 49-67
    • Huang, R.1    Lam, W.2
  • 33
    • 38349172078 scopus 로고    scopus 로고
    • Active semi-supervised fuzzy clustering
    • Grira N., Crucianu M., and Boujemaa N. Active semi-supervised fuzzy clustering. Pattern Recognition 41 5 (2008) 1851-1861
    • (2008) Pattern Recognition , vol.41 , Issue.5 , pp. 1851-1861
    • Grira, N.1    Crucianu, M.2    Boujemaa, N.3
  • 36
    • 40349116323 scopus 로고    scopus 로고
    • Semisupervised clustering with metric learning using relative comparisons
    • Kumar N., and Kummamuru K. Semisupervised clustering with metric learning using relative comparisons. IEEE Transactions on Knowledge and Data Engineering 20 4 (2008) 496-503
    • (2008) IEEE Transactions on Knowledge and Data Engineering , vol.20 , Issue.4 , pp. 496-503
    • Kumar, N.1    Kummamuru, K.2
  • 37
    • 9444294778 scopus 로고    scopus 로고
    • From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering
    • D. Klein, S. Kamvar, C. Manning, From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering, in: The 19th International Conference on Machine Learning, 2002, pp. 307-314.
    • (2002) The 19th International Conference on Machine Learning , pp. 307-314
    • Klein, D.1    Kamvar, S.2    Manning, C.3
  • 43
    • 0442312343 scopus 로고    scopus 로고
    • Fuzzy clustering with a knowledge-based guidance
    • Pedrycz W. Fuzzy clustering with a knowledge-based guidance. Pattern Recognition Letters 25 4 (2004) 469-480
    • (2004) Pattern Recognition Letters , vol.25 , Issue.4 , pp. 469-480
    • Pedrycz, W.1
  • 48
    • 84973587732 scopus 로고
    • A coefficient of agreement for nominal scales
    • Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 1 (1960) 37
    • (1960) Educational and Psychological Measurement , vol.20 , Issue.1 , pp. 37
    • Cohen, J.1
  • 49
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • Rand W.M. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66 (1971) 622-626
    • (1971) Journal of the American Statistical Association , vol.66 , pp. 622-626
    • Rand, W.M.1
  • 53
    • 68549094312 scopus 로고    scopus 로고
    • Information-theoretic distance measures for clustering validation: generalization and normalization
    • Luo P., Xiong H., Zhan G., Wu J., and Shi Z. Information-theoretic distance measures for clustering validation: generalization and normalization. IEEE Transactions on Knowledge and Data Engineering 21 9 (2009) 1249-1262
    • (2009) IEEE Transactions on Knowledge and Data Engineering , vol.21 , Issue.9 , pp. 1249-1262
    • Luo, P.1    Xiong, H.2    Zhan, G.3    Wu, J.4    Shi, Z.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.