Distributed Data Mining of Probabilistic Knowledge

Wai Lam, Alberto Maria Segre

Abstract: We present a distributed approach to data mining of a knowledge representation scheme known as Bayesian belief networks which are capable of dealing with uncertain knowledge. We make use of a machine learning paradigm and a distributed asynchronous search technique to achieve the task of distributed knowledge discovery from data. Our approach boasts a number of features, including dynamic load balancing and fault tolerance. Empirical experiments have been conducted to illustrate its feasibility, solving large scale Bayesian network discovery problems with multiple workstations.

External IDs:dblp:conf/icdcs/LamS97