A practical approach to classify evolving data streams: Training with limited amount of labeled data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 929-934

  Masud, Mohammad M ^a   Gao, Jing ^b   Khan, Latifur ^a   Han, Jiawei ^b   Thuraisingham, Bhavani ^a  

^a The University of Texas at Dallas   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION MODELS; DATA STREAM; EMPIRICAL EVALUATIONS; LABELED DATA; MICRO-CLUSTERS; NEAREST NEIGHBOR ALGORITHM; NOVEL TECHNIQUES; SEMI-SUPERVISED CLUSTERING; STREAM CLASSIFICATION; SYNTHETIC DATA; TRAINING DATA; TRAINING SETS; UNLABELED DATA;

DATA COMMUNICATION SYSTEMS; EDUCATION; INFORMATION MANAGEMENT; LEARNING ALGORITHMS; LEARNING SYSTEMS; MINING;

DATA MINING;

EID: 67049160126     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.152     Document Type: Conference Paper

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