An enhanced pre-FUFP algorithm for incremental mining of association rules

Abstract

Several algorithms have been developed for association rule mining which has now become an interesting field of research in the knowledge discovery domain. Discovery of association rules from large and incremental databases is one of the toughest tasks in data mining. Researchers have been motivated to design innovative and incremental algorithm for association rules mining because the quantity of data available in the real life databases are increasing at a tremendous rate. In this paper, we propose an efficient incremental mining algorithm, called enhanced pre-FUFP algorithm which extends the pre-large item set algorithm further by including the recency concept. The main aim of our proposed approach is to efficiently handle the items that are included recently in the updated database based on adaptive support threshold. At first, the FP-tree is constructed for the old database and then the transactions of incremental database are processed one at a time. After that, the association rules are mined from the updated FP-tree by incorporating an adaptive support. Experiments are performed on extensive real life datasets to compare the performance of our proposed approach with that of the pre-FUFP algorithm. The comparison results show the superiority of our enhanced pre-FUFP algorithm over other existing incremental algorithms.