Multi-agent based decision Support System using Data Mining and Case Based Reasoning

A knowledge-based society determines organizations to focus their activities on improving management quality by using knowledge. Huge data stores become important once the real significance of data is discovered. Data mining techniques are involved in different knowledge processes, as one can notice in various public applications of the researchers. Managers can use these techniques in order to extract patterns, relations, associations from data initially considered of little value. Over the past decade, case-based reasoning (CBR) has emerged as a major research area within the artificial intelligence research field due to both its widespread usage by humans and its appeal as a methodology for building intelligent systems. More recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. This paper focuses on t he synergism between the research areas of Data Mining, CBR System, Multi-agent System and decision support systems (DSSs). A conceptual framework for DSSs based on MAS using DM and CBRS is presented. Nowadays, intelligent agents represent an important opportunity to optimize knowledge management. The research implications of the evolution in the design of DSS based on M AS using DM and CBR systems from automation toward decision-aiding is also