Mining Top-k Frequent-regular Itemsets from Incremental Transactional Database

Abstract

In the past decade, frequent-regular itemset mining (FRIM) has been proposed and applied in a wide range of applications. It aims to discover interesting itemsets frequently and regularly occurring in a static database. However, in real-world applications, the occurrence behavior of items/itemsets may change whenever the database is updated and there may be the situation of overwhelming or none of results generated if the user set inappropriate support threshold. Thus, we here introduce a new approach to mine top-k frequent-regular itemsets from incremental transactional database for mining results which allows users to control the number of results. In this approach, a set of k itemsets having highest frequency of occurrence and regularity occurring in a incremental database is generated. To mine such itemsets, an efficient single-pass algorithm called IMTFRI (Incremental Miner of Top-k Frequent-Regular Itemset) is proposed. The partitioned dynamic bit-vector is utilized to maintain occurrence information of each item/itemsets while mining. In addition, to avoid mining on each incremental database from scratch, the mining with baseline frequency setting technique is designed. Last, experimental studies have been conducted to investigate efficiency of IMTFRI algorithm in the terms of computational time and memory usage.