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

A metapattern-based automated discovery loop for integrated data mining-unsupervised learning of relational patterns

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

2 Author(s)
Wei-Min Shen ; Inf. Sci. Inst., Univ. of Southern California, Marina del Rey, CA, USA ; Bing Leng

A metapattern (also known as a metaquery) is a new approach for integrated data mining systems. As opposed to a typical “toolbox”-like integration, where components must be picked and chosen by users without much help, metapatterns provide a common representation for inter-component communication as well as a human interface for hypothesis development and search control. One weakness of this approach, however, is that the task of generating fruitful metapatterns is still a heavy burden for human users. In this paper, we describe a metapattern generator and an integrated discovery loop that can automatically generate metapatterns. Experiments in both artificial and real-world databases have shown that this new system goes beyond the existing machine learning technologies, and can discover relational patterns without requiring humans to pre-label the data as positive or negative examples for some given target concepts. With this technology, future data mining systems could discover high-quality, human-comprehensible knowledge in a much more efficient and focused manner, and data mining could be managed easily by both expert and less-expert users

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:8 ,  Issue: 6 )

Date of Publication:

Dec 1996

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.