An Exercise on Multi Data Agent with Distributed Data Mining and Related Sequence Modeling

Abstract

The data mining technology has become a way to identify patterns and trends in large amounts of data. Mining technology commonly adopted method of data integration generate data stores, which collect all data in one central location, and then run a algorithm against the data to extract the module prediction and evaluation of useful knowledge. However, a mining technique alone was not tested for every area and data set. Technical data mining involved in such a complex environment must meet high dynamic due to changes in the system can affect the overall system performance. Agent team whose goal is to address complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. The multi-agent systems (MAS) often deal complex applications that require troubleshooting distributed. In many applications the individual and the collective behavior of agents depends on the observed data from distributed sources.