Mining data association based on a revised FP-growth algorithm
Lei Wang, Xing-Juan Fan, Xinglong Liu, Huan Zhao · 2012
This paper introduces a new weighted Apriori based on a revised FP-growth algorithm to mine association rules in a relational database. The new algorithm is acquired by revising the search mechanism of the well known Apriori weighted multidimensional data mining algorithm which searches for candidate item sets by repeatedly scanning in the database. The effectiveness of our proposed algorithm is verified through a real application of mining in the student achievement database.