A Bayesian Hybrid Approach to Unsupervised Time Series Discretization Yoshitaka Kameya Tokyo Institute of Technology 20/Nov/2010 1 TAAI-2010 Gabriel Synnaeve Grenoble University Andrei Doncescu LAAS-CNRS Katsumi Inoue National Institute of Informatics Taisuke Sato Tokyo Institute of Technology
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A Bayesian Hybrid Approach to Unsupervised Time Series ... · TAAI-2010 Prior mean of the Gaussian for level k Weight (Pseudo count) Expected counts of staying at level k Expected
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A Bayesian Hybrid Approach to Unsupervised Time Series Discretization
Yoshitaka KameyaTokyo Institute of Technology
20/Nov/2010 1TAAI-2010
Gabriel SynnaeveGrenoble University
Andrei DoncescuLAAS-CNRS
Katsumi InoueNational Institute of Informatics
Taisuke SatoTokyo Institute of Technology
Outline
• Review: Unsupervised discretization of time series data
– Preliminary experimental results
• Hybrid discretization method based on variational Bayes
• Experimental results
• Summary and future work
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Discretization
• ... converts numeric data into symbolic data
• ... is a preprocessing task in knowledge discovery
• ... may lead to noise reduction and a good data abstraction
– We wish to have interpretable discrete levels
• ... may help symbolic data mining
– Frequent pattern mining
– Inductive logic programming
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Selection
Preprocessing Transformation
Datamining
Interpretation/Evaluation
Target data
Preprocesseddata
Transformeddata
Patterns
Knowledge
[Fayyad et al. 1995]
Unsupervised discretization of time series data
• Binning:– Equal width binning– Equal frequency binning– ...
• Clustering:– Hierarchical clustering [Dimitrova et al. 05]
– K-means– Gaussian mixture models [Mörchen et al. 05b]