Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 28, Issue 1, Pages 181-195Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2015.2462369
Keywords
Time series; distance measures; clustering; multi-label classification
Categories
Funding
- Basque Government [IT-609-13]
- Spanish Ministry of Science and Innovation [TIN2013-41272P]
- NICaiA Project (European Commission) [PIRSES-GA-2009-247619]
- University of the Basque Country UPV/EHU
Ask authors/readers for more resources
In the past few years, clustering has become a popular task associated with time series. The choice of a suitable distance measure is crucial to the clustering process and, given the vast number of distance measures for time series available in the literature and their diverse characteristics, this selection is not straightforward. With the objective of simplifying this task, we propose a multi-label classification framework that provides the means to automatically select the most suitable distance measures for clustering a time series database. This classifier is based on a novel collection of characteristics that describe the main features of the time series databases and provide the predictive information necessary to discriminate between a set of distance measures. In order to test the validity of this classifier, we conduct a complete set of experiments using both synthetic and real time series databases and a set of five common distance measures. The positive results obtained by the designed classification framework for various performance measures indicate that the proposed methodology is useful to simplify the process of distance selection in time series clustering tasks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available