Time series k-means: A new k-means type smooth subspace clustering for time series data

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• 作者：Xiaohui Huang ; ^a ; ^b ; ^{hxh016@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Yunming Ye^b ; Liyan Xiong^a ; Raymond Y.K. Lau^c ; Nan Jiang^a ; Shaokai Wang^b
• 关键词：Time series ; k-means clustering ; Subspace clustering ; Feature selection ; Data mining
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：1 November 2016
• 年：2016
• 卷：367-368
• 期：Complete
• 页码：1-13
• 全文大小：1066 K

文摘

Existing clustering algorithms are weak in extracting smooth subspaces for clustering time series data. In this paper, we propose a new k-means type smooth subspace clustering algorithm named Time Series k-means (TSkmeans) for clustering time series data. The proposed TSkmeans algorithm can effectively exploit inherent subspace information of a time series data set to enhance clustering performance. More specifically, the smooth subspaces are represented by weighted time stamps which indicate the relative discriminative power of these time stamps for clustering objects. The main contributions of our work include the design of a new objective function to guide the clustering of time series data and the development of novel updating rules for iterative cluster searching with respect to smooth subspaces. Based on a synthetic data set and five real-life data sets, our experimental results confirm that the proposed TSkmeans algorithm outperforms other state-of-the-art time series clustering algorithms in terms of common performance metrics such as Accuracy, Fscore, RandIndex, and Normal Mutual Information.