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

CrossMine: efficient classification across multiple database relations

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)

Most of today's structured data is stored in relational databases. Such a database consists of multiple relations which are linked together conceptually via entity-relationship links in the design of relational database schemas. Multirelational classification can be widely used in many disciplines, such as financial decision-making, medical research, and geographical applications. However, most classification approaches only work on single "flat" data relations. It is usually difficult to convert multiple relations into a single flat relation without either introducing huge, undesirable "universal relation" or losing essential information. Previous works using inductive logic programming approaches (recently also known as relational mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they suffer from poor scalability w.r.t. the number of relations and the number of attributes in databases. We propose CrossMine, an efficient and scalable approach for multirelational classification. Several novel methods are developed in CrossMine, including (1) tuple ID propagation, which performs semantics-preserving virtual join to achieve high efficiency on databases with complex schemas, and (2) a selective sampling method, which makes it highly scalable w.r.t. the number of tuples in the databases. Both theoretical backgrounds and implementation techniques of CrossMine are introduced. Our comprehensive experiments on both real and synthetic databases demonstrate the high scalability and accuracy of CrossMine.

Published in:

Data Engineering, 2004. Proceedings. 20th International Conference on

Date of Conference:

30 March-2 April 2004

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.