Scalable model-based clustering for large databases based on data summarization

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 11, 2005, Pages 1710-1719

  Jin, Huidong ^a   Wong, Man Leung ^b   Leung, Kwong Sak ^c  

^a CSIRO   (Australia)

^b LINGNAN UNIVERSITY   (Hong Kong)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Data summary; Expectation maximization; Gaussian mixture model; Maximum penalized likelihood estimate; Scalable clustering

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; DATABASE SYSTEMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PATTERN RECOGNITION;

CLUSTERING ALGORITHMS; DATA SUMMARIZATION; EXPECTATION-MAXIMIZATION; GAUSSIAN MIXTURE MODEL; MAXIMUM PENALIZED LIKELIHOOD ESTIMATE; SCALABLE CLUSTERING;

DATA MINING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER SIMULATION; EVALUATION; FACTUAL DATABASE; INFORMATION RETRIEVAL; METHODOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DATABASES, FACTUAL; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 28044431838     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.226     Document Type: Article

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