By Topic

Mining Frequent Itemsets over Data Streams with Multiple Time-Sensitive Sliding Windows

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
$33 $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

4 Author(s)

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Frequent pattern is a kind of data mining techniques discovered knowledge and has been widely studied over the last decade. There are several models and approaches for mining such knowledge, but all previous works only consider a static length of sliding window for mining frequent itemsets. We propose a multiple slidng windows for mining frequent patterns on data stream in this paper. The details of study scope are as follows. We propose an efficient discounting method with different lengths of time-sensitive sliding-window. This discounting method doesn't lose the information about Acount and also saves much memory space. Finally, we implement and evaluate the proposed algorithms for mining frequent itemsets on data stream.

Published in:

Advanced Language Processing and Web Information Technology, 2007. ALPIT 2007. Sixth International Conference on

Date of Conference:

22-24 Aug. 2007