中文摘要：

提出了一种基于Normal矩阵的时间序列聚类方法。该算法首先对时间序列数据进行向量形式转换,计算出各个时间序列间的相似度并构建复杂网络,然后利用基于Normal矩阵的方法进行复杂网络社团划分,同一类的时间序列被划分到一个社团,即实现对时间序列数据的聚类。为了验证该方法的可行性和有效性,将其应用于股票时间序列数据聚类分析中,并在两个实际的数据集上与其他方法相比较,取得了较好的实验结果。

英文摘要：

This paper presented a method for time series clustering based on the spectral bisection method of Normal matrix. The algorithm transformed time series data into vector forms firstly,calculated the similarity between any pairs of time series and constructed complex network. Then the complex network would be divided into communities by using of the method of Normal matrix. The time series were clustered in terms of the results of partitioning network. Finally,in order to verify the feasibility and effectiveness of the presented method,analyzed the real world stock time series,compared other methods on two real datasets and obtained the reasonable results.