By Topic

An Efficient Approximate Approach to Mining Frequent Itemsets over High Speed Transactional Data Streams

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

3 Author(s)
Kuen-Fang Jea ; Dept. of Comput. Sci. & Eng., Nat. Chung-Hsing Univ., Taichung ; Chao-Wei Li ; Tsui-Ping Chang

A data stream is a massive and unbounded sequence of data elements that are continuously generated at a fast speed. Compared with traditional data mining, knowledge discovery in data streams is more challenging since several requirements need to be satisfied. In this paper we propose a mining algorithm for finding frequent itemsets over a transactional data stream. Unlike most of existing algorithms, our method works based on the theory of Approximate Inclusion-Exclusion to approximate the itemsets' counts. Some techniques are designed and integrated into the algorithm for performance improvement. And the performance of the proposed algorithm is tested and analyzed through several experiments.

Published in:

Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on  (Volume:3 )

Date of Conference:

26-28 Nov. 2008