Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Domain ontology driven data mining: a medical case study
Proceedings of the 2007 international workshop on Domain driven data mining
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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We present a novel approach for assisting pattern interpretation by data mining end-users: finding explanations for association rules based on probabilistic dependencies. In the approach, relevant variables are selected from rules and from other data sources to facilitate human-understandable interpretations. An explanation of a rule involves consideration of observable variables in the data and alignment with the conditional probability of the rule. To build explanations involving multiple, interacting variables, we use Bayesian networks to structure relationships. We illustrate the benefits of our technique for assisting pattern interpretation using Internet use survey data. The novel technique has potential in various data mining scenarios such as computer aided pattern interpretation and interactive data mining.