Finding Explanations for Assisting Pattern Interpretation
Yen-Ting Kuo, Liz Sonenberg, Andrew J. Lonie · 2008
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.