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» T-Time: Threshold-Based Data Mining on Time Series
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KDD
1999
ACM
117views Data Mining» more  KDD 1999»
13 years 12 months ago
Identifying Distinctive Subsequences in Multivariate Time Series by Clustering
Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a di erent problem, determining what distinguish...
Tim Oates
SDM
2009
SIAM
127views Data Mining» more  SDM 2009»
14 years 4 months ago
Event Discovery in Time Series.
The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient’s electrocardiog...
Carla E. Brodley, Dan Preston, Pavlos Protopapas
BMCBI
2007
139views more  BMCBI 2007»
13 years 7 months ago
Significance analysis of microarray transcript levels in time series experiments
Background: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over ...
Barbara Di Camillo, Gianna Toffolo, Sreekumaran K....
EDBT
2004
ACM
142views Database» more  EDBT 2004»
14 years 7 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...
SIGMOD
2008
ACM
236views Database» more  SIGMOD 2008»
14 years 7 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...