Paper Info
Title
RedTrees: A relational decision tree algorithm in streams
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.
Year
DOI
Venue
2010
10.1016/j.eswa.2010.02.096
Expert Syst. Appl.
Keywords
Field
DocType
declarative bias,single stream,data ming,hottest research topic,multi-relational data stream,good efficiency,data synopsis method,decision tree,period sampling,counterpart algorithm,multi-relational data streams,relational decision tree algorithm,pattern refinement,data mining,relational data
Decision tree,Data mining,Data stream mining,Computer science,Artificial intelligence,Streaming data,Sampling (statistics),STREAMS,Decision tree learning,Machine learning,Incremental decision tree
Journal
Volume
Issue
ISSN
37
9
Expert Systems With Applications
Citations 
PageRank 
References 
6
0.46
10
Authors
4
Name
Order
Citations
PageRank
Wei Hou160.46
Bingru Yang218626.67
Chensheng Wu3142.12
Zhun Zhou4252.07