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.
|Number of pages||4|
|Journal||Lecture Notes in Computer Science|
|Publication status||Published - 30 Sep 2005|
|Event||5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States of America|
Duration: 22 May 2005 → 25 May 2005