OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2007, Pages 448-452

  Spinosa, Eduardo J ^a   De Carvalho, André Ponce De Leon F ^a   Gama, Joo ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF PORTO   (Portugal)

Author keywords

Clustering; Concept drift; Data streams; Novelty detection

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; PROBLEM SOLVING; REAL TIME SYSTEMS;

CONCEPT DRIFTS; DATA STREAMS; NOVELTY DETECTION; SINGLE LEARNING STRATEGY;

DATA PROCESSING;

EID: 35248892821     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1244002.1244107     Document Type: Conference Paper

References (5)

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4. D. M. J. Tax. DDtools, the data description toolbox for matlab. http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html, March 2005. version 1.1.2.
5. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1):69-101, 1996.