Abstract
Mining association rules is an important data mining problem. A fast binary partition-based algorithm (BPA) for mining association rules in large databases is presented in this paper. Basically, the framework of BPA is similar to that of the algorithm Apriori. In the first pass, all the frequent 1-item sets are divided into two disjoint parts. Accordingly, in each subsequent pass k, we partition the set of all the frequent k-item sets into three subsets. Any two different partitions are disjoint. If necessary, this partitioning procedure can be a recursive one. Therefore, we get a binary partition tree in the first pass and a corresponding ternary partition tree in each subsequent pass k. Due to such a partition, BPA can be very easily parallelized, assuming a shared-memory architecture.
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.
