Discovering original motifs with different lengths from time series

Finding previously unknown patterns in a time series has received much attention in recent years. Of the associated algorithms, the k-motif algorithm is one of the most effective and efficient. It is also widely used as a time series preprocessing routine for many other data mining tasks. However, t...

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Bibliographic Details
Published inKnowledge-based systems Vol. 21; no. 7; pp. 666 - 671
Main Authors Tang, Heng, Liao, Stephen Shaoyi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.10.2008
Subjects
Data mining
Motif
Pattern discovery
Time series
Data mining
Pattern discovery
Motif
Time series
Online AccessGet full text
ISSN0950-7051
1872-7409
DOI10.1016/j.knosys.2008.03.022

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Summary:Finding previously unknown patterns in a time series has received much attention in recent years. Of the associated algorithms, the k-motif algorithm is one of the most effective and efficient. It is also widely used as a time series preprocessing routine for many other data mining tasks. However, the k-motif algorithm depends on the predefine of the parameter w, which is the length of the pattern. This paper introduces a novel k-motif-based algorithm that can solve the existing problem and, moreover, provide a way to generate the original patterns by summarizing the discovered motifs.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2008.03.022