Mining informative rule set for prediction over a sliding window

Authors:
Nguyen Dat Nhan;Nguyen Thanh Hung;Le Hoai Bac
Affiliations:
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam;High School for the Gifted, Vietnam National University, Ho Chi Minh City, Vietnam;Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Year:
2010

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Abstract

We study the problem of mining informative (association) rule set for prediction over data streams. On dense datasets and low minimum support threshold, the generating of informative rule set does not use all mined frequent itemsets (FIs). Therefore, we will waste a portion of FIs if we run existing algorithms for finding FIs from data streams as the first stage to mine informative rule set. We propose an algorithm for mining informative rule set directly from data streams over a sliding window. Our experiments show that our algorithm not only attains high accurate results but also out performs the two-stage process, find FIs and then generate rules, of mining informative rule set.