Multiagent Data Mining Systems: An Overview

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. The Data Mining technology normally adopts data integration method to generate Data warehouse, on which to gather all data into a central site, and then run an algorithm against that data to extract the useful Module Prediction and knowledge evaluation. However, a single data-mining technique has not been proven appropriate for every domain and data set. Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. In many applications the individual and collective behavior of the agents depends on the observed data from distributed sources. The field of Distributed Data Mining (DDM) deals with these challenges in analyzing distributed data and offers many algorithmic solutions to perform different data analysis and mining operations in a fundamentally distributed manner that pays careful attention to the resource constraints. Since multi-agent systems are often distributed and agents have proactive and reactive features which are very useful for Knowledge Management Systems, combining DDM with MAS for data intensive applications is appealing. This document is intended to serve as an introduction to the field of Multi-Agent Data Mining and as an overview of several Agent-based Data Mining Frameworks. The overview is not exhaustive, but it highlights how multiagent systems can be and have been used to build complex Data Mining systems.