Multi-Agents Approach for Data Mining Based k-Means for Improving the Decision Process in the ERP Systems

Nadjib Mesbahi, Okba Kazar, Saber Benharzallah, Merouane Zoubeidi and Samir Bourekkache
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Nadjib Mesbahi: Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria
Okba Kazar: Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria
Saber Benharzallah: Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria
Merouane Zoubeidi: Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria
Samir Bourekkache: Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria

International Journal of Decision Support System Technology (IJDSST), 2015, vol. 7, issue 2, 1-14

Abstract: Today the enterprise resource planning (ERP) became a tool that enables uniform and consistent management of information system (IS) of the company with a large single database. In addition, Data Mining is a technology whose purpose is to promote information and knowledge extraction from a large database. In this paper, an agent-based multi-layered approach for data mining based k-Means through the ERP to extract hidden knowledge in the ERP database is proposed. To achieve this, the authors call the paradigm of multi-agent system to distribute the complexity of several autonomous entities called agents, whose goal is to group records or observations on similar objects classes using the k-means technique that is dedicated the task of clustering. This will help business decision-makers to take good decisions and provide a very good response time by the use of multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and agents comply with the specifications FIPA.

Date: 2015
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