计算机科学技术学报(英文版)
計算機科學技術學報(英文版)
계산궤과학기술학보(영문판)
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
2015年
4期
859-873
,共15页
周竞%朱山风%黄晓地%张彦春
週競%硃山風%黃曉地%張彥春
주경%주산풍%황효지%장언춘
time series analysis%clustering%dynamic programming%information search and retrieval
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.