C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
A Distributed Data Mining System for a Novel Ubiquitous Healthcare Framework
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Distributed Data Mining in a Ubiquitous Healthcare Framework
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Distributed Data Mining (DDM) generally deals with the mining of data within a distributed framework such as local area and wide area networks. One strong case for DDM systems is the need to mine for patterns in very large databases. This requires mandatory partitioning or splitting of databases into smaller sets which can be mined locally over distributed hosts. Data Distribution implies communication costs associated with the need to combine the results from processing local databases. This paper considers the development of a DDM system on a cluster. In specific we approach the problem of data partitioning for data mining. We present a prototype system for DDM using a data partitioning mechanism based on Bayesian mixture modeling. Results from comparison with standard techniques show plausible support for our system and its applicability.