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Mining Motifs in Massive Time Series Databases (2002)
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| Venue: | In Proceedings of IEEE International Conference on Data Mining (ICDM’02 |
| Citations: | 49 - 3 self |
BibTeX
@INPROCEEDINGS{Patel02miningmotifs,
author = {Pranav Patel and Eamonn Keogh and Jessica Lin and Stefano Lonardi},
title = {Mining Motifs in Massive Time Series Databases},
booktitle = {In Proceedings of IEEE International Conference on Data Mining (ICDM’02},
year = {2002},
pages = {370--377}
}
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Abstract
The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification.
