Abstract
In recent years, the amount of data into a geometric growth puts forward higher requirements on data mining algorithm. In the process of frequent itemsets of traditional Apriori algorithm produced, frequent itemsets' generation and storage are quite a waste of time and space. In this paper, we put forward a new Hash table and use the technology to improve the algorithm and get SamplingHT algorithm, through a lot of contrast experiments showed that the new algorithm enhances performance when frequent itemset is generated, and effectively reduce the database scan times, In order to achieve more optima.
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 Database Theory and Application
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.
