Enhancing time series clustering by incorporating multiple distance measures with semi-supervised learning

Jing Zhou, Shanfeng Zhu, Xiaodi Huang, Yanchun Zhang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.
Original languageEnglish
Pages (from-to)859-873
Number of pages15
JournalJournal of Computer Science and Technology
Volume30
Issue number4
DOIs
Publication statusPublished - Jul 2015

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