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 the 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 MAS using DM and
CBR systems from automation toward decision-aiding is also
explored.
Keywords: Multi-agent system, Data Mining, Case Based Reasoning, Decision Support System, Supply Chain Management
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