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Vienna, Austria
July 3, 2006 to July 5, 2006
ISBN: 0-7695-2590-3
pp: 251-260
Johannes Assfalg , University of Munich, Germany
Hans-Peter Kriegel , University of Munich, Germany
Peer Kroger , University of Munich, Germany
Peter Kunath , University of Munich, Germany
Alexey Pryakhin , University of Munich, Germany
Matthias Renz , University of Munich, Germany
ABSTRACT
The issue of data mining in time series databases is of utmost importance for many practical applications and has attracted a lot of research in the past years. In this paper, we focus on the recently proposed concept of threshold similarity which compares the time series based on the time frames within which they exceed a user-defined amplitude threshold ? . We propose a novel approach for cluster analysis of time series based on adaptable threshold similarity. The most important issue in threshold similarity is the choice of the threshold ? . Thus, the threshold ? is automatically adapted to the characteristics of a small training dataset using the concept of support vector machines. Thus, the optimal ? is learned from a small training set in order to yield an accurate clustering of the entire time series database. In our experimental evaluation we demonstrate that our cluster analysis using adaptable threshold similarity can be successfully applied to many scientific real-world data mining applications.
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CITATION
Johannes Assfalg, Hans-Peter Kriegel, Peer Kroger, Peter Kunath, Alexey Pryakhin, Matthias Renz, "Time Series Analysis Using the Concept of Adaptable Threshold Similarity", SSDBM, 2006, Scientific and Statistical Database Management, International Conference on, Scientific and Statistical Database Management, International Conference on 2006, pp. 251-260, doi:10.1109/SSDBM.2006.53
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