Discovering the intrinsic cardinality and dimensionality of time series using MDL

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 1086-1091

  Hu, Bing ^a   Rakthanmanon, Thanawin ^a   Hao, Yuan ^a   Evans, Scott ^b   Lonardi, Stefano ^a   Keogh, Eamonn ^a  

^a University of California Riverside   (United States)

^b GE GLOBAL RESEARCH   (United States)

Author keywords

Dimensionality reduction; MDL; Time series

Indexed keywords

ABSTRACTION LEVEL; CARDINALITIES; CRITICAL STEPS; DIMENSIONALITY REDUCTION; EMPIRICAL TEST; INTRINSIC FEATURES; MDL; MINIMAL DESCRIPTION LENGTH; MULTI-RESOLUTIONS; OUTLIER DISCOVERY; REPRESENTATION MODEL; TIME SERIES DATA MINING; TIME-SERIES DATA;

ABSTRACTING; APPROXIMATION ALGORITHMS; DATA MINING; TIME SERIES;

CLUSTERING ALGORITHMS;

EID: 84857158294     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.54     Document Type: Conference Paper

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