基于EMD与K-means算法的时间序列聚类  

Clustering Method of Time Series Based on EMD and K-means Algorithm

作　　者：刘慧婷[1] 倪志伟[2] 

机构地区：[1]安徽大学计算机科学与技术学院,合肥230039 [2]合肥工业大学计算机网络系统研究所,合肥230009

出　　处：《模式识别与人工智能》2009年第5期803-808,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家863计划资助项目(No.2007AA04Z116);国家自然科学基金项目(No.70871033);安徽高校省级自然科学研究项目(KJ2007B303ZC)资助

摘　　要：有效实现时间序列聚类的重要前提是序列的维数得到约简,序列中包含的噪声能够被滤除.文中提出一种能够对时间序列进行有效预处理的方法.该方法先通过经验模态分解实现时间序列趋势的提取,再利用自底向上算法对趋势序列进行分段,最后转换成由{-1,0,1}构成的齐序列.为了证明该方法既能实现降维,也可实现数据序列中噪声的滤除,文中利用K-means算法对经过上述方法预处理后的序列进行聚类.实验结果表明,与直接对原序列进行聚类相比,对预处理后的数据序列进行聚类,空间复杂度较低、准确性较高.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.

关 键 词：时间序列 分段序列 降维 经验模态分解 K-MEANS算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]