Incremental Learning Algorithm for association rule Mining

These Association rule mining is to find association rules that satisfy the predefined minimum support and confidence from a given database. The Apriori and FP-tree algorithms are the most common and existing frequent itemsets mining algorithm, but these algorithms lack incremental learning ability. Incremental learning ability is desirable to solve the temporal dynamic property of knowledge and improve the performance of the mining process as the incremental data is available with the passage of time. Currently FUFP, pre-FUFP and IMBT algorithms have been developed that support incremental learning. The IMBT uses a binary tree data structure called an Incremental mining binary tree. This work proposes a novel incremental learning algorithm that makes use of a data structure called Item-Itemset(I-Is) tree that is a variation of B+ tree. Initially I-Is tree is created from the original data to allow searching of frequent items based on the threshold values. The created I-Is tree is updated incrementally.