Research Article | OPEN ACCESS
A Similarity Measure Method for Symbolization Time Series
Qiang Niu and Zhigang Li
Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Research Journal of Applied Sciences, Engineering and Technology 2013 5:1726-1730
Received: July 27, 2012 | Accepted: September 03, 2012 | Published: February 11, 2013
Abstract
Similarity measure is the base task of time series data mining tasks. LCSS measure method has obvious limitations in the two different length time series selection of a linear function. The ELCS measure method is proposed to normalize the sequence, which introducing the scale factor to limit the search path of the similarity matrix. Experiment in hierarchical clustering algorithm shows that the improved measure makes up for the shortcomings of LCSS, improves the efficiency and accuracy of clustering and improves time complexity.
Keywords:
Hierarchical cluster, LCSS, similarity measure, time series,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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