Extending k-means-based algorithms for evolving data streams with variable number of clusters

Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011

Volumn 2, Issue , 2011, Pages 14-19

  De Andrade Silva, Jonathan ^a   Hruschka, Eduardo Raul ^a,b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Clustering; Data Stream; Online Clustering

Indexed keywords

ALGORITHMIC FRAMEWORK; CLUSTERING; CLUSTERING DATA; COMPUTATIONALLY EFFICIENT; DATA PARTITION; DATA STREAM; K-MEANS; NUMBER OF CLUSTERS; ONLINE-CLUSTERING; REAL WORLD DATA; STATE-OF-THE-ART ALGORITHMS; STATISTICAL SIGNIFICANCE; VARIABLE NUMBER OF CLUSTERS;

DATA COMMUNICATION SYSTEMS; LEARNING SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 84857808949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2011.67     Document Type: Conference Paper

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