A Generic and Extendible Multi-Agent Data Mining Framework

A generic and extendible Multi-Agent Data Mining (MADM) framework, EMADS (the Extendible Multi-Agent Data mining System) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a system of wrappers. The advantage offered is that the system is easily extendible, so that further data agents and mining agents can simply be added to the system. A demonstration EMADS framework is currently available. The paper includes details of the EMADS architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework’s operation is provided by considering two MADM scenarios. 1 Motivation and Goals Multi-Agent Data Mining (MADM) seeks to harness the general advantages of Multi-Agent Systems (MAS) in the application domain of Data Mining (DM). MAS technology has much to offer DM, particularly in the context of various forms of distributed and cooperative DM. The main issues with MADM are th...

• Type: Conference
• Year: 2009
• Where: HAIS
• Authors: Kamal Ali Albashiri, Frans Coenen
• Keywords: Artificial Intelligence, Data Mining System, Extendible Multi-agent Data, HAIS 2009, Multi-agent Data Mining