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Volumn 22, Issue , 2012, Pages 814-822

A nonparametric Bayesian model for multiple clustering with overlapping feature views

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; CLUSTER ANALYSIS;

EID: 84877752825     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (21)

References (24)
  • 1
    • 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. of the IEEE Int'l Conf. on Data Mining, pages 53-62, 2006.
    • (2006) Proc. of the IEEE Int'l Conf. on Data Mining , pp. 53-62
    • Bae, E.1    Bailey, J.2
  • 3
    • 84867186048 scopus 로고    scopus 로고
    • Variational inference for Dirichlet process mixtures
    • D. M. Blei and M. I. Jordan. Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1 (1): 121-144, 2006.
    • (2006) Bayesian Analysis , vol.1 , Issue.1 , pp. 121-144
    • Blei, D.M.1    Jordan, M.I.2
  • 6
    • 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 SIAM Int'l Conf. on Data Mining, pages 118-129, 2010.
    • (2010) SIAM Int'l Conf. on Data Mining , pp. 118-129
    • Dang, X.H.1    Bailey, J.2
  • 10
    • 38149119574 scopus 로고    scopus 로고
    • Bayesian nonparametric latent feature models (with discussion)
    • Oxford, UK July
    • Z. Ghahramani, T. Griffiths, and P. Sollich. Bayesian nonparametric latent feature models (with discussion). In Bayesian Statistics 8, pages 201-226, Oxford, UK, July 2007.
    • (2007) Bayesian Statistics , vol.8 , pp. 201-226
    • Ghahramani, Z.1    Griffiths, T.2    Sollich, P.3
  • 24
    • 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. Journal on Machine Learning Research, 3: 583-617, 2002.
    • (2002) Journal on Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.