By Topic

Hypothesis Testing Based Knowledge Discovery in Distributed Multiple Data Sources

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

4 Author(s)
Shilei Bai ; Sch. of Inf. Eng., Commun. Univ. of China, Beijing, China ; Hui Ren ; Wei Jiang ; Yujian Jiang

In the past several years, most data mining researchers focus on data mining from single data source. Nowadays, data mining from multiple data sources is a new problem in Web environment and is also an efficient technique for solving knowledge discovery in distributed databases. A new method for mining multi-data sources is presented in this paper. By sharing knowledge patterns discovered in other similar data sources, hypothesis testing is employed for verifying whether the patterns are also suitable for local data source or not. So that can improve the efficiency of KDD greatly. Finally the effectiveness of this method is analyzed and experimental result is given. This method can be extended as an efficient data mining algorithm in case of apriori hypothesizes are provided. And it can be also used for incremental data mining.

Published in:

Internet Technology and Applications, 2010 International Conference on

Date of Conference:

20-22 Aug. 2010