Agent based frequent set meta mining: Introducing EMADS

Abstract

In this paper we: introduce EMADS, the Extendible Multi-Agent Data mining System, to support the dynamic creation of communities of data mining agents; explore the capabilities of such agents and demonstrate (by experiment) their application to data mining on distributed data. Although, EMADS is not restricted to one data mining task, the study described here, for the sake of brevity, concentrates on agent based Association Rule Mining (ARM), in particular what we refer to as frequent set meta mining (or Meta ARM). A full description of our proposed Meta ARM model is presented where we describe the concept of Meta ARM and go on to describe and analyse a number of potential solutions in the context of EMADS. Experimental results are considered in terms of: the number of data sources, the number of records in the data sets and the number of attributes represented. © 2008 International Federation for Information Processing.