The Efficient Mining of Skyline Patterns from a Volunteer Computing Network

In the ever-growing world, concepts of High-utility Itemset Mining (HUIM) as well Frequent (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these only used one factor to estimate an itemset. past, skyline pattern mining by considering both aspects frequency and utility has extensively discussed. most cases, however, people tend focus on purchase quantities itemsets rather than frequencies. this article, we propose a new called quantity-utility (SQUP) provide better estimations decision-making process quantity together. Two algorithms, respectively, SQU-Miner SKYQUP presented efficiently mine set SQUPs. Moreover, usage volunteer computing is proposed show potential real supermarket applications. efficient utility-max structures also mentioned for reduction candidate itemsets, utilized SKYQUP. These two store upper-bound under constraint instead constraint, second structure moreover applies recursive updated further obtain strict utility. Our in-depth experimental results prove that stronger performance when comparison made terms memory usage, runtime, number candidates.