The problem of eficiently 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 solvedproblem. However. from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previourly unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogv to their discrete counterparts in computation biologv. An eflcient motif discovery algorithm far time series would be usefur 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 classijication. In this work we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an eflcient algorithm to discover them, and we demonstrate the utilih, and eficiencv o f our amroach on several real world
Initially, motifs were defined to be the most frequently occurring patterns in a timeseries REF .
Mining motifs in massive time series databases
2002 IEEE International Conference on Data Mining, 2002. Proceedings.
2002 IEEE International Conference on Data Mining, 2002. Proceedings.
Computer Science