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

• Type: Conference
• Year: 2006
• Where: DEXA
• Authors: Sungbo Seo, Jaewoo Kang, Dongwon Lee, Keun Ho Ryu
• Keywords: Classification Algorithms, Continuous Multivariate Stream, Database, DEXA 2006, Multivariate Stream Data