Degree Type
Creative Component
Semester of Graduation
Spring 2018
Department
Computer Science
First Major Professor
Jin Tian
Degree(s)
Master of Science (MS)
Major(s)
Computer Science
Abstract
Bayesian networks are being used in various domains, such as data mining, diagnosis, bioinformatics/computational biology, etc. One problem associated with Bayesian networks is to learn their structures from training data. In this paper, we introduce a new approach to structural learning of Bayesian networks, based on hierarchical clustering. We learn the network in hierarchical stages, learning over a subset of the random variables at each stage. Experiments show that this approach learns Bayesian networks faster as compared to curriculum-based learning methods. We show a comparison of our networks with curriculum based learned Bayesian networks over different evaluation metrics as well. Also, performance of hierarchical clustering vs an existing ordering-based algorithm is observed.
Copyright Owner
Nikhita Sharma
Copyright Year
2018
File Format
application/pdf
Recommended Citation
Sharma, Nikhita, "Hierarchical clustering based structural learning of Bayesian networks" (2018). Creative Components. 11.
https://lib.dr.iastate.edu/creativecomponents/11
