Knowledge discovery in databases aims at finding useful information, which can be deployed for decision making. The problem of high utility itemset mining has specifically garnered huge research focus in the past decade, as it aims to find the patterns from the databases that conform to an objective utility function. Several algorithms exist in literature to mine the high utility items from the databases; however, most of them require large execution time and have high memory consumption. In this paper, we propose a new algorithm, R-Miner, based on a novel data structure, called the residue maps, that stores the utility information of an item directly and is used for the mining process. Several experiments are undertaken to assess the efficacy of the proposed algorithm against the benchmark algorithms. The experimental results indicate that the R-Miner algorithm outperforms the state-of-the-art mining algorithms.
翻译:在数据库中发现知识的目的是寻找有用的信息,可以用于决策;在过去十年中,高功用物品集采矿问题特别引起了巨大的研究焦点,因为其目的是从符合客观效用功能的数据库中找到符合客观效用功能的模式;文献中存在几种算法,用来从数据库中开采高功用物品;然而,其中大多数需要大量的执行时间和高记忆消耗;在本文件中,我们提议一种新的算法,即R-Miner,以新的数据结构为基础,称为残余地图,直接储存某一物品的效用资料,并用于采矿过程;进行了若干试验,以评估提议的算法对基准算法的效力;实验结果表明,R-Miner算法优于最先进的采矿算法。