An investigation into the issues of multi-agent data mining

Very often data relevant to one search is not located at a single site, it may be widely-distributed and in many different forms. Similarly there may be a number of algorithms that may be applied to a single Knowledge Discovery in Databases (KDD) task with no obvious “best” algorithm. There is a clear advantage to be gained from a software organisation that can locate, evaluate, consolidate and mine data from diverse sources and/or apply a diverse number of algorithms. Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. Since MAS are often distributed and agents have proactive and reactive features, combining Data Mining (DM) with MAS for Data Mining (DM) intensive applications is therefore appealing. This thesis discusses a number of research issues concerned with the viability of Multi-Agent systems for Data Mining (MADM). The problem addressed by this thesis is that of investigating the usefulness of MAS in the context of DM. This thesis also examines the issues affecting the design and implementation of a generic and extendible agent-based data mining framework. The principal research issues associated with MADM are those of experience and resource sharing, flexibility and extendibility, and protection of privacy and intellectual property rights. To investigate and evaluate proposed solutions to MADM issues, an Extendible Multi-Agent Data mining System (EMADS) was developed. This framework promotes the ideas of high-availability and high performance without compromising data or DM algorithm integrity. The proposed framework provides a highly flexible and extendible data-mining platform. The resulting system allows users to build collaborative DM approaches. The proposed framework has been applied to a number of DM scenarios. Experimental tests on real data have confirmed its effectiveness.