Distributed Classification Using Oiki Ddm Model
نویسنده
چکیده
Distributed Data Mining is the process of extracting hidden knowledge from distributed data sources. A number of DDM architectures are proposed during the last few years. Recently, Optimized Incremental Knowledge Integration Distributed Data Mining model is proposed. It is a mobile-agent based model that overcomes the drawbacks of the traditional models. In this paper, the classification mining is formulated using this new model in order to make it scalable to large amounts of data. The proposed model uses meta-learning methodology to integrate the generated knowledge.
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