Collective Data Mining From Distributed , Vertically PartitionedFeature

This paper develops collective data mining, a unique approach for nding patterns from a network of databases, each with a distinct feature space. This paper addresses both distributed cooperative learning at the global level and also learning at the local data sites. In addition to developing the foundation of the collective data mining , it also presents BODHI, a distributed data mining (DDM) system that implements the collective data mining approach. Although the architecture is ideal for accommodating diierent in-ductive learning algorithms for data analysis at diierent sites, this paper suggests one scalable approach using a gene expression based evolutionary algorithm. This approach is used for distributed fault detection in an electrical power distribution network. Experimental results demonstrating the success of the developed system are also presented.

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