A residual utility-based concept for high-utility itemset mining

Knowledge discovery in databases aims at finding useful information for decision-making. The problem of high-utility itemset mining (HUIM) has specifically garnered huge research attention, as it aims to find relevant information on patterns in a database, which conform to a user-defined utility function. The mined patterns are used for making data-backed decisions in the fields of healthcare, e-commerce, web analytics, etc. Various algorithms exist in the literature related to mining the high-utility items from the databases; however, most of them require multiple database scans, or deploy complex data structures. The utility-list is an efficient list-based data structure that is being widely adopted in the design of HUIM algorithms. The existing utility-list-based algorithms, however, suffer from some drawbacks like extensive use of inefficient join operations, multiple definitions of join operations, etc. Though the HUIM is an important research area, yet very little research has been directed towards improving the design of data structures used for the mining process. In this paper, we introduce the concept of residual utility to design two new data structures, called residue-map and master-map. Using these two data structures, a new algorithm, called R-Miner, is introduced for mining the high-utility items. In order to further optimise the mining process, the cumulative utility value is used as an upper bound and additional pruning conditions are also discussed. Several experiments are carried out on both real and synthetic datasets to compare the performance of R-Miner with the existing list-based algorithms. The experimental results show that the R-Miner improves the performance by up to the order of 2 as compared to the list-based algorithms: EFIM, H-Miner, HUI-Miner, FHM, and ULB-Miner.