Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

	Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning
	Zhou Jing 1; Zhu ShanFeng 1; Huang Xiaodi 3; Zhang Yanchun 1
	2015
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	30 期号:4 页码:859
摘要	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.
关键词	DIFFERENT REPRESENTATIONS EDIT DISTANCE CLASSIFICATION SEGMENTATION ENSEMBLE time series analysis clustering dynamic programming information search and retrieval
语种	英语
资助项目	[National Natural Science Foundation of China] ; [National Key Technology Research and Development Program of China]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/32686
专题	中国科学院计算技术研究所期刊论文_中文
作者单位	1.复旦大学 2.中国科学院计算技术研究所 3.查尔斯史都华大学 4.Victoria University, Sch Engn & Sci, Melbourne, Vic 8001, Australia
推荐引用方式 GB/T 7714	Zhou Jing,Zhu ShanFeng,Huang Xiaodi,et al. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2015,30(4):859.
APA	Zhou Jing,Zhu ShanFeng,Huang Xiaodi,&Zhang Yanchun.(2015).Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,30(4),859.
MLA	Zhou Jing,et al."Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 30.4(2015):859.