Paper Info
Title
Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning
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.
Year
DOI
Venue
2015
10.1007/s11390-015-1565-7
Journal of Computer Science and Technology
Keywords
Field
DocType
time series analysis, clustering, dynamic programming, information search and retrieval
Data mining,Canopy clustering algorithm,Fuzzy clustering,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Consensus clustering,Artificial intelligence,Cluster analysis,Machine learning
Journal
Volume
Issue
ISSN
30
4
1860-4749
Citations 
PageRank 
References 
4
0.40
31
Authors
4
Name
Order
Citations
PageRank
Jing Zhou140.40
Shanfeng Zhu242935.04
Xiaodi Huang334240.33
Yanchun Zhang43059284.90