Abstract
Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mining the maximum frequent patterns. Firstly, applying the intersection theory of mathematics, put forwards a number of concepts and definitions. Then gives the process of association rules mining, and analyzes its performance. After that, the example describes the process of implementation of the algorithm. Finally, the experimental results show that the algorithm ISMFP is efficient on mining frequent patterns, especially there exists low threshold of support degree or long patterns.
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.
