VALMOD: Scalable Discovery of Variable-Length Motifs in Data Series
Michele Linardi*,Yan Zhu+
Themis Palpanas* and Eamonn Keogh+*(Lipade, University Paris Descartes) +(UC, Riverside)
This page is the support page of VALMOD, which mainly complements the experimental evaluation, providing the relative materials. You may find the VALMOD article here: VALMOD 2018 (ACM Sigmod conference) .
Empirical evaluation of our approach
Please note that we compared VALMOD with other motif discovery approaches:
You can find several experimental "ready-to-run" bash scripts in this ARCHIVE . Both the zip files (source code and experiments scripts) are protected by password. Please contact me at michele[dot]linardi[at]orange[dot]fr.
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