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中文摘要：
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．
英文摘要：
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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