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DEXA
2006
Springer

Multivariate Stream Data Classification Using Simple Text Classifiers

9 years 2 days ago
Multivariate Stream Data Classification Using Simple Text Classifiers
We introduce a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes as input a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a simple text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Na
Sungbo Seo, Jaewoo Kang, Dongwon Lee, Keun Ho Ryu
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DEXA
Authors Sungbo Seo, Jaewoo Kang, Dongwon Lee, Keun Ho Ryu
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