We propose a framework, called MIC, which adopts an information-theoretic approach to address the problem of quantitative association rule mining. In our MIC framework, we first d...
This paper presents a numerical association rule extraction method that is based on original quality measures which evaluate to what extent a numerical classification model behave...
Abstract. Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and th...
The mining of informative rules calls for methods that include different attributes (e.g., weights, quantities, multipleconcepts) suitable for the context of the problem to be an...