TY - JOUR
T1 - A novel two-level clustering method for time series data analysis
AU - Lai, Cheng Ping
AU - Chung, Pau Choo
AU - Tseng, Vincent S.
N1 - Funding Information:
This research was supported by Ministry of Economic Affairs, Taiwan, ROC under Grant No. 97-EC-17-A-02-s1-024 and by National Science Council, Taiwan, ROC under Grant No. NSC 97-3114-E-006 -001 .
PY - 2010/9
Y1 - 2010/9
N2 - Clustering analysis has been applied in a wild variety of fields such as biology, medicine, economics, etc. For time series clustering, dimension reduction methods like data sampling or piecewise aggregate approximation (PAA) algorithm are often applied to reduce data dimension before clustering. Consequently, the information of subsequence may be overlooked. Nevertheless, some properties of time series with the same sampling data may result in different clustering results after considering the subsequence information. In this paper, we propose a novel two-level clustering method named 2LTSC (two-level time series clustering), which considers both the whole time series, denoted as level-1 in the first level, and the subsequence information of time series, denoted as level-2 in the second level. The data length of level-2 could be different and thus is also considered in the second level in the proposed 2LTSC method. Through experimental evaluation, it is shown that the proposed two-level clustering method, which considers two different time granules at the same time, can provide different and deeper viewpoints for time series clustering analysis.
AB - Clustering analysis has been applied in a wild variety of fields such as biology, medicine, economics, etc. For time series clustering, dimension reduction methods like data sampling or piecewise aggregate approximation (PAA) algorithm are often applied to reduce data dimension before clustering. Consequently, the information of subsequence may be overlooked. Nevertheless, some properties of time series with the same sampling data may result in different clustering results after considering the subsequence information. In this paper, we propose a novel two-level clustering method named 2LTSC (two-level time series clustering), which considers both the whole time series, denoted as level-1 in the first level, and the subsequence information of time series, denoted as level-2 in the second level. The data length of level-2 could be different and thus is also considered in the second level in the proposed 2LTSC method. Through experimental evaluation, it is shown that the proposed two-level clustering method, which considers two different time granules at the same time, can provide different and deeper viewpoints for time series clustering analysis.
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U2 - 10.1016/j.eswa.2010.02.089
DO - 10.1016/j.eswa.2010.02.089
M3 - Article
AN - SCOPUS:80051606484
SN - 0957-4174
VL - 37
SP - 6319
EP - 6326
JO - Expert Systems With Applications
JF - Expert Systems With Applications
IS - 9
ER -