A novel two-level clustering method for time series data analysis

Authors:
Cheng-Ping Lai;Pau-Choo Chung;Vincent S. Tseng
Affiliations:
Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Taiwan, ROC and Department of Medical Informatics, National Cheng Kung Un ...;Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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.