Abstract
One of the central problems in knowledge discovery in databases, relies on the very large number of rules that classic rule mining systems extract. This problem is usually solved by means of a post-processing step, that alters the entire volume of extracted rules, in order to output only a few potentially interesting ones. This article presents a new approach that allows the user to explore action rules space locally, without the need to extract and post-process all action rules from a database. This solution is based on rule schemas, a new formalism designed to improve the representation of user beliefs and expectations, and on a novel algorithm for local action rules mining based on schemas.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Data Mining, Modelling and Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
