Distributed data mining methodology for clustering and classification model

Authors:
Marcin Gorawski;Ewa Pluciennik-Psota
Affiliations:
Silesian University of Technology, Institute of Computer Science, Gliwice, Poland;Silesian University of Technology, Institute of Computer Science, Gliwice, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

Distributed computing and data mining are nowadays almost ubiquitous. Authors propose methodology of distributed data mining by combining local analytical models (built in parallel in nodes of a distributed computer system) into a global one without necessity to construct distributed version of data mining algorithm. Different combining strategies for clustering and classification are proposed and their verification methods as well. Proposed solutions were tested with data sets coming from UCI Machine Learning Repository.