Journal article
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and information systems, Vol.6(2), pp.164-187
03/2004
Handle:
https://hdl.handle.net/2376/114402
Abstract
We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.
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Details
- Title
- Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
- Creators
- R Chen - School of Electrical Engineering and Computer Science Washington State University Pullman, WA 99164-2752 USAK Sivakumar - School of Electrical Engineering and Computer Science Washington State University Pullman, WA 99164-2752 USAH Kargupta - Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA
- Publication Details
- Knowledge and information systems, Vol.6(2), pp.164-187
- Academic Unit
- Voiland College of Engineering and Architecture
- Publisher
- Springer-Verlag; London
- Identifiers
- 99900548020901842
- Language
- English
- Resource Type
- Journal article