Dimension reduction for clustering time series using global characteristics

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

Research output: Contribution to journalConference articleResearchpeer-review

20 Citations (Scopus)

Abstract

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
Pages (from-to)792-795
Number of pages4
JournalLecture Notes in Computer Science
Volume3516
Issue numberIII
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

Cite this

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abstract = "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.",
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Dimension reduction for clustering time series using global characteristics. / Wang, Xiaozhe; Smith, Kate A.; Hyndman, Rob J.

In: Lecture Notes in Computer Science, Vol. 3516, No. III, 30.09.2005, p. 792-795.

Research output: Contribution to journalConference articleResearchpeer-review

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AU - Smith, Kate A.

AU - Hyndman, Rob J.

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AB - 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.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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