频繁项集挖掘的研究进展及主流方法

关联分析作为数据挖掘的主要研究模块之一,主要用于发现隐藏在大型数据集中的强关联特征。而多数关联规则挖掘任务可分为频繁模式(频繁项集、频繁序列、频繁子图)的产生和规则的产生。前者发现数据集中满足最小支持度阈值的项集、序列与子图；后者从上一步发现的频繁模式中提取高置信度的规则。频繁项集挖掘是许多数据挖掘任务中的关键问题,也是关联规则挖掘算法的核心。十几年来,学者们致力于提高频繁项集的生成效率,从不同的角度进行改进以提高算法效率,大量的高效可伸缩性算法被提出。文中对频繁项集挖掘进行深入分析,对完全频繁项集、闭频繁项集、极大频繁项集的典型算法进行介绍和评述,最后对频繁项集挖掘算法的研究方向进行简要分析。

Research Progress and Mainstream Methods of Frequent Itemsets Mining

As one of the main research modules of data mining,association analysis is mainly used to find strong correlation features hidden in large data sets.The majority of association rule mining tasks can be divided into generation of frequent patterns (frequent itemsets,frequent sequences,frequent subgraphs) and generation of rule.The former finds itemsets,sequences,and subgraphs satisfying the minimum support threshold in the dataset.The latter extracts high confidence rules from the frequent patterns found in the previous step.Frequent itemset mining is a key issue in many data mining tasks,and it is also the core of association rule mining algorithms.For more than a decade,scholars have devoted themselves to improving the efficiency of generating frequent itemsets,improving algorithms from different perspectives,so as to improve the efficiency of algorithms,and a large number of efficient and scalable algorithms have been proposed.This article makes an in-depth analysis of frequent item set mining,introduces and reviews the typical algorithms of complete frequent itemsets,closed frequent itemsets,and maximal frequent itemsets.Finally,the research direction of frequent itemsets mining algorithm was briefly analyzed.