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›› 2015, Vol. 30 ›› Issue (4): 859-873.

• Special Section on Data Management and Data Mining •

### Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

Jing Zhou1,2(周竞), Shan-Feng Zhu1,2(朱山风), Member, CCF, ACM, Xiaodi Huang3(黄晓地), Member, ACM, IEEE, Yanchun Zhang1,4,5(张彦春), Member, CCF

1. 1. School of Computer Science, Fudan University, Shanghai 200433, China;
2. Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China;
3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia;
4. School of Engineering and Science, Victoria University, Melbourne, Victoria 8001, Australia;
5. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai 201203, China
• Received:2015-02-01 Revised:2015-03-25 Online:2015-07-05 Published:2015-07-05
• About author:Jing Zhou received his B.S. degree in computer science from Donghua University, Shanghai, in 2012. He is currently a graduate student of the Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai. His current research interests include time series analysis, data mining, and bioinformatics.
• Supported by:

The work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61332013, 61272110, and 61370229, and the National Key Technology Research and Development Program of China under Grant No. 2013BAH72B01.

Time series clustering is widely applied in various areas. Existing research work focuses mainly on distance measures between two time series, such as DTW-based (dynamic time warping) methods, edit distance-based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering data sets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given data set of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To our best knowledge, this work demonstrates for the first time that semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series data sets, we show that our method outperforms individual measures, as well as typical integration approaches.

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