Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple data sources. However, faces several problems in terms autonomy, privacy, performance implementation. requires homogeneity regarding environment, control, administration classification algorithm(s), such that requirements are too strict inflexible many applications. In this paper, we propose employment Multi-Agent System (MAS) be combined (MAS-DDM). MAS is mechanism for creating goal-oriented autonomous agents within shared environments communication coordination facilities. We shall show MAS-DDM both desirable beneficial. MAS-DDM, could communicate their beliefs (calculated classification) by covering private non-sharable other decide whether use classifying instances adjusting prior assumptions about each class data. will develop modified Naive Bayesian algorithm because (1) shown most used uncertain (2) even if all same algorithm, preforms better than approaches non-communicating processes. Point provide an evidence exchange information between helps increasing accuracy task significantly.