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| Clustering Method of Time Series Based on EMD and K-means Algorithm |
| LIU Hui-Ting1, NI Zhi-Wei2 |
1.School of Computer Science and Technology, Anhui University, Hefei 230039
2.Institute of Computer Network System, Hefei University of Technology, Hefei 230009 |
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Abstract Dimension reduction of time series and noise in sequences filtering are important prerequisites for effective realization of time series clustering. A method is proposed to preprocess time series effectively. Firstly, the trend of a time sequence is got by using empirical mode decomposition method. Then, the trend series are divided into several segments by bottom-up algorithm. Finally, the piecewise series are translated into uniform sequences, and each of them is composed of -1, 0 and 1. To prove that the proposed method can achieve dimensionality reduction and filter out the noise from the data sequence, K-means algorithm is utilized to finish clustering of pretreated time series. Experimental results show clustering of pretreated data sequences is better than that of the original series.
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Received: 10 October 2008
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2009, Vol. 22 
