Abstract
Abstract Aiming at the performance bottleneck of traditional Apriori algorithm when the data set is slightly large, this paper adopts the idea of parallelization and improves the Apriori algorithm based on MapReduce model. Firstly, the local frequent itemsets on each sub node in the cluster are calculated, then all the local frequent itemsets are merged into the global candidate itemsets, and finally, the frequent itemsets that meet the conditions are filtered according to the minimum support threshold. The advantage of the improved algorithm is that it only needs to scan the transaction database twice and calculate the frequent item set in parallel, which improves the efficiency of the algorithm.
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 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.
