DocumentCode :
3338162
Title :
Time series analysis based on enhanced NLCS
Author :
Nie, Dacheng ; Fu, Yan ; Zhou, Junlin ; Fang, Yuke ; Xia, Hu
Author_Institution :
Dept. of Software, Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2010
fDate :
23-25 June 2010
Firstpage :
292
Lastpage :
295
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.
Keywords :
Algorithm design and analysis; Clustering algorithms; Computer science; Data mining; Electronic mail; Euclidean distance; Shafts; Speech recognition; Time measurement; Time series analysis; Clustering; Normalized longest common subsequence; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Interaction Sciences (ICIS), 2010 3rd International Conference on
Conference_Location :
Chengdu, China
Print_ISBN :
978-1-4244-7384-7
Electronic_ISBN :
978-1-4244-7386-1
Type :
conf
DOI :
10.1109/ICICIS.2010.5534754
Filename :
5534754
Link To Document :
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