Distributed data mining of probabilistic knowledge

W. Lam; A.M. Segre

doi:10.1109/ICDCS.1997.598026

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Conference proceeding

Distributed data mining of probabilistic knowledge

W. Lam and A.M. Segre

Proceedings of 17th International Conference on Distributed Computing Systems, pp.178-185

1997

DOI: 10.1109/ICDCS.1997.598026

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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.

Data Mining

Knowledge Representation

Bayesian methods

Data engineering

Fault tolerance

Knowledge engineering

Load management

Research and development management

Systems engineering and theory

Workstations

Details

Title: Subtitle: Distributed data mining of probabilistic knowledge
Creators: W. Lam - Chinese University of Hong Kong
A.M. Segre - Chinese University of Hong Kong
Resource Type: Conference proceeding
Publication Details: Proceedings of 17th International Conference on Distributed Computing Systems, pp.178-185
Publisher: IEEE
DOI: 10.1109/ICDCS.1997.598026
ISSN: 1063-6927
eISSN: 2575-8411
Language: English
Date published: 1997
Academic Unit: Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science
Record Identifier: 9984370460902771

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