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
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.
刘慧婷，倪志伟. 基于EMD与K-means算法的时间序列聚类*[J]. 模式识别与人工智能, 2009, 22(5): 803-808.
LIU Hui-Ting, NI Zhi-Wei. Clustering Method of Time Series Based on EMD and K-means Algorithm. , 2009, 22(5): 803-808.
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