A Novel Progressive Sampling based Approach for Effective Mining of Association Rules
V. Umarani, M. Punithavalli · International Journal of Computer Applications · 2010
Mining Association Rules from huge databases is one of the important issue that need to be addressed.This paper presents a new sampling based association rule mining algorithm that uses a progressive sampling approach based on negative border and Frequent pattern growth (FP Growth) algorithm for finding the candidate item sets which ultimately shortens the execution time in generating the candidate itemsets.Experimental results reveals that the propsed approach is significantly more efficient than the Apriori based sampling approach.