NDLI: Time series analysis based on enhanced NLCS

Content Provider	IEEE Xplore Digital Library
Author	Dacheng Nie Yan Fu Junlin Zhou Yuke Fang Hu Xia
Copyright Year	2010
Description	Author affiliation: Department of Software, University of Electronic Science and Technology of China, Chengdu, China (Dacheng Nie) \|\| Department of Computer Science and Engineering, University of Electronic Science, and Technology of China, Chengdu, China (Yuke Fang) \|\| Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China (Yan Fu; Junlin Zhou; Hu Xia)
Abstract	Similarity analysis plays a key role in clustering of time series. Normalized longest common subsequence (NLCS) is a similarity measurement widely used in comparing character sequences. In this paper, we developed the NLCS and present a novel algorithm to precisely calculate the similarity of time series. The algorithm used the sum of all common subsequence instead of longest common subsequence which can not represent the similarity of sequences accurately. The experiments based on synthetic and real-life datasets shown that the proposed algorithm performed better in comparing the similarity of time series. Comparing with Euclidean distance on four cluster validity indices, the results lead to a better performance by k-means or self-organize map.
Starting Page	292
Ending Page	295
File Size	163067
Page Count	4
File Format	PDF
ISBN	9781424473847
e-ISBN	9781424473861
DOI	10.1109/ICICIS.2010.5534754
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Algorithm design and analysis Shafts Time series analysis Clustering algorithms Euclidean distance Speech recognition Normalized longest common subsequence Time measurement Electronic mail Data mining Clustering
Content Type	Text
Resource Type	Article

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