Knowledge-Driven Computing pp 273-288 | Cite as
A Query-Driven Exploration of Discovered Association Rules
Summary
The paper concerns the presentation phase of a knowledge discovery process with use of association rules. The rules, once obtained, have normally to be explained and interpreted in order to make use of them. The authors propose an approach based on the employment of Predictive Model Markup Language (PMML) to facilitate an environment for the systematic examination of complex mining models. The PMML is an XML application developed by the Data Mining Group dedicated to data analysis models. We start with a short description of PMML, and show an example of an automatically encoded mining model. Then XQuery language is involved to demonstrate how to explore a model by querying its PMML structure. Preliminary results for a real association rule model are presented in the final part of the paper. Three approaches are considered: (1) simple direct querying of the PMML structure of the discovered model, (2) interactive browsing the rule base using set-theoretic operations, (3) automatic query formulation with genetic programming.
Keywords
Association Rule Rule Base Frequent Itemsets Association Rule Mining Minimum Support ThresholdPreview
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