Dimension reduction for clustering time series using global characteristics

Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

24 Citations (Scopus)


Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2005
Subtitle of host publication5th International Conference Atlanta, GA, USA, May 22-25, 2005 Proceedings, Part I
EditorsVaidy S. Sunderam, Geert Dick van Albada, Peter M.A. Sloot, Jack J. Dongarra
Place of PublicationBerlin Germany
Number of pages4
ISBN (Print)3540260323, 9783540260325
Publication statusPublished - 30 Sep 2005
Event5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States of America
Duration: 22 May 200525 May 2005

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference5th International Conference on Computational Science - ICCS 2005
CountryUnited States of America
CityAtlanta, GA

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