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Kernel-Tree: Mining Frequent Patterns in a Data Stream Based on Forecast Support
David Tse Jung Huang1, Yun Sing Koh1, Gillian Dobbie1, and Russel Pears2
1Department of Computer Science, University of Auckland, New Zealand
dhua035@aucklanduni.ac.nz
ykoh@cs.auckland.ac.nz
gill@cs.auckland.ac.nz
2School of Computing and Mathematical Sciences, AUT University, New Zealand
rpears@aut.ac.nz
Abstract. Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure.
Keywords: Data Streams, Kernel Regression, Frequent Pattern Mining
LNAI 7691, p. 614 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012