HOT SAX: Efficiently finding the most unusual time series subsequence

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 8-15

  Keogh, Eamonn ^a   Lin, Jessica ^a   Fu, Ada ^b  

^a University of California Riverside   (United States)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Anomaly detection; Clustering; Time series data mining

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; MEDICINE; SPACE SHUTTLES; SPACE SURVEILLANCE; TELEMETERING;

ANOMALY DETECTION; CLUSTERING; DATA CLEANING; INTUITIVE PARAMETER;

TIME SERIES ANALYSIS;

EID: 34548547034     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.79     Document Type: Conference Paper

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