Discovering original motifs with different lengths from time series

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)666-671
Journal / PublicationKnowledge-Based Systems
Issue number7
Publication statusPublished - Oct 2008


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. © 2008 Elsevier B.V. All rights reserved.

Research Area(s)

  • Data mining, Motif, Pattern discovery, Time series