RedTrees: A relational decision tree algorithm in streams

Authors:
Wei Hou;Bingru Yang;Chensheng Wu;Zhun Zhou
Affiliations:
Data Movie Technology, China Research Institute of Film Science and Technology, Beijing 100086, China;School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China;Beijing Municipal Institute of Science and Technology Information, Beijing 100037, China;School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Classification of streaming data is one of the hottest research topics in data mining nowadays, many efforts had been dedicated to relative researches for the single stream. However, to the best of our knowledge, there is no counterpart algorithm for the multi-relational data streams up to now. In this paper, one data synopsis method, which is compatible with the scenario of multi-relational data streams, is introduced. Based on period sampling, this method could avoid multiple join operations at some extent. Pursuantly, an algorithm for constructing decision tree from multi-relational data streams, RedTrees, is proposed. Then, the declarative bias in RedTrees, JoinTree, which makes the pattern refinement more efficient, is discussed. The theoretical analysis and experiments prove its effectiveness and good efficiency.