BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering original motifs with different lengths from time series
Knowledge-Based Systems
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Locating motifs in time-series data
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Time series motif is a previously unknown pattern appearing frequently in a time series. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing massive time series databases as well as many other advanced time series data mining tasks. In this paper, we propose a new efficient algorithm, called EP-BIRCH, for finding motifs in large time series datasets. This algorithm is more efficient than MK algorithm and stable to the changes of input parameters and these parameters are easy to be determined through experiments. The instances of a discovered motif may be of different lengths and user does not have to predefine the length of the motif.