Bayesian clustering of fuzzy feature vectors using a quasi-likelihood approach

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 1, 2009, Pages 74-85

  Marttinen, Pekka ^a   Tang, Jing ^a   De Baets, Bernard ^b   Dawyndt, Peter ^b   Corander, Jukka ^c  

^a UNIVERSITY OF HELSINKI   (Finland)

^b GHENT UNIVERSITY   (Belgium)

^c ÅBO AKADEMI UNIVERSITY   (Finland)

Author keywords

Bayesian clustering; Continuous data; Fuzzy modeling; Quasi likelihood

Indexed keywords

BAYESIAN NETWORKS; CLASSIFIERS; FLOW OF SOLIDS; INFORMATION THEORY; LEARNING SYSTEMS; VECTORS;

ANALYTICAL EXPRESSIONS; BAYESIAN CLUSTERING; BAYESIAN FRAMEWORKS; BAYESIAN MODELS; CLUSTERING SOLUTIONS; CLUSTERINGS; CONTINUOUS DATA; DATA FEATURES; DISCRETIZATION; EMPIRICAL DATUMS; FUZZY FEATURES; FUZZY MODELING; GAUSSIAN MIXTURES; NUMERICAL PROPERTIES; POSTERIOR PROBABILITIES; QUASI-LIKELIHOOD; SCIENCE AND ENGINEERINGS; STATISTICAL POWERS; UNSUPERVISED CLASSIFICATIONS; WIDE SPECTRUMS;

FUZZY CLUSTERING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER SIMULATION; FUZZY LOGIC; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; FUZZY LOGIC; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 57149130667     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.53     Document Type: Article

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