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EDBT
2004
ACM
142views Database» more  EDBT 2004»
14 years 9 months ago
Iterative Incremental Clustering of Time Series
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property...
Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dim...
DKE
2008
83views more  DKE 2008»
13 years 9 months ago
Mining fuzzy periodic association rules
We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two...
Wan-Jui Lee, Jung-Yi Jiang, Shie-Jue Lee
SIGMOD
2008
ACM
236views Database» more  SIGMOD 2008»
14 years 9 months ago
Approximate embedding-based subsequence matching of time series
A method for approximate subsequence matching is introduced, that significantly improves the efficiency of subsequence matching in large time series data sets under the dynamic ti...
Vassilis Athitsos, Panagiotis Papapetrou, Michalis...
PKDD
2005
Springer
188views Data Mining» more  PKDD 2005»
14 years 3 months ago
Elastic Partial Matching of Time Series
We consider a problem of elastic matching of time series. We propose an algorithm that automatically determines a subsequence b of a target time series b that best matches a query ...
Longin Jan Latecki, Vasilis Megalooikonomou, Qiang...
SIGMOD
2007
ACM
186views Database» more  SIGMOD 2007»
14 years 9 months ago
An efficient and accurate method for evaluating time series similarity
A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specifi...
Michael D. Morse, Jignesh M. Patel