多维数据流最大频集挖掘模型和算法

A Model and an Algorithm to Mine Maximal Frequent Itemsets From Multidimensional Data Stream

摘要: 为了挖掘到有价值的信息,需要挖掘多维数据流上的频繁项目集,因此引入多维项目和多维项目集的概念表示多维数据流上的项目.设计了一种紧凑、压缩的数据结构MaxFP-Tree用于维护多维项目集,并在MaxFP-Tree的基础上设计了挖掘多维数据流上最大频集的增量式更新算法.实验结果表明,设计的挖掘多维数据流中最大频集的模型和算法是高效的.

Abstract: In order to get valuable information,mining frequent itemsets from multidimensional data stream is needed.Through introduction of the concept of multidimensional item and multidimensional itemsets,the multidimensional data stream is expressed.A compact,compressed data structure MaxFP-Tree is designed to maintain multidimensional sets.Based on MaxFP-Tree,an incremental update algorithm to mine maximal frequent multidimensional itemsets is given.Experiment results show that the model and the algorithm of mining multidimensional data streams are efficient.