Abstract
Mining generalized association rules between items in the presence of the taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert to discover some more interesting but much less supported association rules. In our, previous work, we have addressed this problem and proposed two algorithms, MMS Cumulate and MMS Stratify. In this paper, we examine the problem of maintaining the discovered multi-support, generalized association rules when new transactions are added into the original database. We propose an algorithm MMS UP. Empirical evaluation showed that MMS UP is 2-6 times faster than running MMS Cumulate or MMS-Stratify on the updated database afresh.
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 callDisclaimer: 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.
