Dimension reduction for clustering time series using global characteristics

 

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.

Cite

@inproceedings{WSH05,
  address   = {22-25 May 2005. Atlanta, GA, USA},
  author    = {Xiaozhe Wang and Kate A Smith-Miles and Rob J Hyndman},
  booktitle = {Lecture Notes in Computer Science, Vol.3516. Proceedings, Computational Science -- {ICCS} 2005: 5th International Conference},
  doi       = {10.1007/11428862_108},
  pages     = {792--795},
  publisher = {Springer},
  title     = {Dimension reduction for clustering time series using global characteristics},
  volume    = {3516},
  year      = 2005
}