By Topic

Analyzing the subjective interestingness of association rules

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
$33 $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)
Bing Liu ; Nat. Univ. of Singapore, Singapore ; Wynne Hsu ; Shu Chen ; Yiming Ma

Association rules, a class of important regularities in databases, have proven very useful in practical applications, but association-rule-mining algorithms tend to produce huge numbers of rules, most of which are of no interest. Users have considerable difficulty manually analyzing so many rules to identify the truly interesting ones. To solve that problem, we have developed a new approach to help them find interesting rules (in particular, unexpected rules) from a set of discovered association rules. This interestingness analysis system (IAS) leverages the user's existing domain knowledge to analyze discovered associations and then rank discovered rules according to various interestingness criteria, such as conformity and various types of unexpectedness. This article describes how we have implemented this technique and used it successfully in a number of applications.

Published in:

IEEE Intelligent Systems and their Applications  (Volume:15 ,  Issue: 5 )