Probabilistic Relational Data Mining
Sudeshna Sen. Probabilistic Relational Data Mining. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (9):8-14, 2011. Full text available. BibTeX
@article{key:article,
author = {Sudeshna Sen},
title = {Probabilistic Relational Data Mining},
journal = {IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET)},
year = {2011},
number = {9},
pages = {8-14},
note = {Full text available}
}
Abstract
Bayesian Networks(BN) have been considered to be one of the most widely used probabilistic data modelling and propositional uncertainty processing paradigms. They exploit the underlying conditional independences in the domain by providing compact graphical representations for high-dimensional joint distributions. A BN consists of two components - a directed acyclic graph whose nodes correspond to a pre-specified set of attributes or random variables; and a set of conditional probability distributions (CPDs) over the attributes. The techniques that have been developed for learning BNs from data have been shown to be remarkably effective for some data mining problems, especially probabilistic descriptive data mining.
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