Cart (Loading....) | Create Account
Close category search window
 

Ontology Learning by Clustering Based on Fuzzy Formal Concept Analysis

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

3 Author(s)
Wen Zhou ; Shanghai Univ., Shanghai ; Zong-tian Liu ; Yan Zhao

Ontology is an important tool of knowledge representation, but its construction is a difficult and tedious task. Ontology constructed by formal concept analysis is quite complicated in terms of the number of concepts generated and can not deal with the vague and uncertain information in practice. A new method is developed to create fuzzy ontology by clustering on Fuzzy Formal Concept Analysis. In the end, experimental results on artificially generated datasets are produced which shows that the learning algorithm has excellent performance on the time-spatial complexity.

Published in:

Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International  (Volume:1 )

Date of Conference:

24-27 July 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.