By Topic

A hierarchical distributed data mining architecture

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)
Bin Liu ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Shu-Gui Cao ; Qing-Chun Li ; Qi Li

Current distributed data mining (DDM) systems popularly assume distributed data sources as partitions of a virtual data table and separately mine them. In fact, when there is essential difference among data sources, the assumption will fail and DDM result quality will also be damaged. For this issue, a hierarchical DDM architecture is proposed by grouping data sources according to their similarity. Ontology technology is adopted to depict the essential content of data sources and measure their similarity.

Published in:

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on  (Volume:1 )

Date of Conference:

10-13 July 2011