增量数据上的闭合定量高效用项集挖掘算法

Closed high utility quantitative itemset mining algorithm on incremental data

高效用项集(HUI)挖掘能够提供数据集中高利润的项的组合信息,有利于在现实应用中制定有效的营销策略。然而,HUI仅提供项集及其总效用,不提供单个项的购买数量,而现实场景中项的数量能提供更精准的信息。因此,研究者提出定量高效用项集(HUQI)挖掘算法。针对当前的HUQI挖掘算法仅能处理静态数据且存在结果集冗余的问题,提出增量更新的定量效用列表结构来存储并更新数据集中项的效用信息,并基于该结构提出一种挖掘闭合定量高效用项集(CHUQI)的算法。将所提出的算法与FHUQI-Miner(Faster High Utility Quantitative Itemset Miner)算法在结果集数量、最小效用阈值、批次数目以及可扩展性上对比时间与内存消耗。实验结果表明,所提算法能够有效处理增量数据,挖掘出更有趣的项集。

High Utility Itemset(HUI)mining can provide information about the combination of highly profitable items in a dataset,which is useful for developing effective marketing strategies in real-world applications.However,HUIs only provide the itemsets and their total utility,not the purchased numbers of individual items,and the numbers of items in a real scenarios provide more precise accurate information.Therefore,High Utility Quantitative Itemset(HUQI)mining algorithms have been proposed by researchers.Focusing on the issue that the current HUQI mining algorithms can only process static data and have the problem of redundant resultsets,an incrementally updated quantitative utility list structure was proposed for storing and updating the utility information of items in the dataset,and based on this structure,an algorithm for mining Closed High Utility Quantitative Itemset(CHUQI)was proposed.The time and memory consumption of the proposed algorithm was compared with that of Faster High Utility Quantitative Itemset Miner(FHUQI-Miner)algorithm in terms of the number of result sets,minimum utility threshold,number of batches,and scalability.Experimental results show that the proposed algorithm can process incremental data effectively and mine more interesting itemsets.