Title :
Subsequence time series (STS) clustering techniques for meaningful pattern discovery
Author_Institution :
Mitsubishi Electr. Res. Labs., Cambridge, MA, USA
fDate :
April 18-21, 2005
Keywords :
data mining; pattern clustering; pattern matching; time series; minimum shift distance; pattern alignment; pattern discovery; sliding window; subsequence time series clustering; trivial match; Clustering methods; Laboratories; Pattern matching; Sociotechnical systems;
Conference_Titel :
Integration of Knowledge Intensive Multi-Agent Systems, 2005. International Conference on
Print_ISBN :
0-7803-9013-X
DOI :
10.1109/KIMAS.2005.1427109
