A new incremental relational association rules mining approach

摘要

Online data miningtechniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams.Relational association rules(RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a newIncremental Relational Association Rule Mining(IRARM) approach with the aim of progressively adapting the interestingrelational association rulesidentified in a data set, when it is enlarged with new instances. We have experimentally evaluatedIRARMon publicly available data sets. The reduction in mining time when usingIRARMagainst mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension.