Volume 4, Issue 1, June 2019, Page: 32-45

A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence

Gholamreza Soleimany, Department of Industrial Engineering, Yazd University, Yazd, Iran

Masoud Abessi, Department of Industrial Engineering, Yazd University, Yazd, Iran

Masoud Abessi, Department of Industrial Engineering, Yazd University, Yazd, Iran

Received: May 3, 2019;
Accepted: Jun. 3, 2019;
Published: Jun. 20, 2019

DOI: 10.11648/j.ajdmkd.20190401.16 View 37 Downloads 22

Abstract

In this research, a new similarity measurement method that named Developed Longest Common Subsequence (DLCSS) is suggested for time series data mining. The main idea of the DLCSS is using the logic of the Longest Common Subsequence (LCSS) method and the concept of similarity in time series data. In most studies related to time series data mining, referred to the LCSS and Dynamic Time Warping (DTW) methods as the best and most usable for similarity measurement methods, but the LCSS is intrinsically designed to measure the similarity of two sequences of character, which later was developed for time series by defining and determining the similarity threshold. The value of similarity threshold has huge impact on the quality of time series data mining. In the DLCSS by defining two similarity thresholds and determining the values of them, this defect is eliminated. The performance of the DLCSS will be compared with the LCSS and DTW in time series data mining by the Query by content and K-medoids Clustering techniques on 23 datasets from the UCR datasets. The result shows that it is possible to claim that the performance of the DLCSS is better than the LCSS and DTW with 90% confidence.

Keywords

Time Series, Data Mining, Similarity Measurement, Longest Common Subsequence, Dynamic Time Warping, Developed Longest Common Subsequence

To cite this article

Gholamreza Soleimany,
Masoud Abessi,
A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence,

*American Journal of Data Mining and Knowledge Discovery*. Vol. 4, No. 1, 2019, pp. 32-45. doi: 10.11648/j.ajdmkd.20190401.16Copyright

Copyright © 2019 Authors retain the copyright of this article.

This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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