一种基于DTW的符号化时间序列聚类算法

Symbolization time series clustering based on DTW

提出了一种基于DTW的符号化时间序列聚类算法,对降维后得到的不等长符号时间序列进行聚类。该算法首先对时间序列进行降维处理,提取时间序列的关键点,并对其进行符号化;其次利用DTW方法进行相似度计算;最后利用Normal矩阵和FCM方法进行聚类分析。实验结果表明,将DTW方法应用在关键点提取之后的符号化时间序列上,聚类结果的准确率有较好大提高。

A method of clustering symbolization time series based on DTW is proposed to cluster the unequal dimensional time series obtained by reduction. The key points of the time series are firstly extracted and symbolized. Then the similarity between the two time series is calculated by DTW method. Lastly, the normal matrix and FCM algorithm are employed to cluster the time series. The experimental results show that the accuracy of cluster result obtained by the proposed method is good.