By Topic

Online data stream Mining of Recent Frequent Itemsets based on Sliding Window model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jia-Dong Ren ; Coll. of Inf. Sci. & Eng., YanShan Univ., Qinhuangdao ; Ke Li

Online data stream mining is one of the most important issues in data mining. Identifying the recent knowledge can provide valuable information for the analysis of the data stream. In this paper, we proposed an one-pass data stream mining algorithm to mine the recent frequent itemsets in data streams with a sliding window basing on transactions. To reduce the cost of time and memory needed to slide the windows, each items is denoted a bit-sequence representations. Basing on a priori property, this kind of representations can find frequent items in data streams efficiently. We named this method MRFI-SW (mining recent frequent itemsets by sliding window) algorithm. Experiment results show that the proposed algorithm not only attains highly accurate mining result, but also consumes less memory than existing algorithms for mining frequent itemsets over recent data streams.

Published in:

Machine Learning and Cybernetics, 2008 International Conference on  (Volume:1 )

Date of Conference:

12-15 July 2008