Multi-agents based data mining for intelligent decision support systems

Recent rapid development of computer technology has introduced a data explosion challenge. In addition, data mining and multi agent techniques are known as a very popular approach for dealing with complex datasets. Such a hybrid approach can be considered as an effective approach for the development of intelligent decision support systems in health domain. In this paper we propose an improved data mining and multi agent technique called DMMA, which uses a real time agent mining approach to mine large datasets in a distributed environment. This study found that the processing speed is improved as the result of the multi-agent mining approach, although there can be a corresponding marginal loss of accuracy. This loss of accuracy gap tends to close over time as more data becomes available.