Identifying Most Predictive Items
Daniel A. Keim, Markus Wawryniuk · 2004
Frequent itemsets and association rules are generally accepted concepts in analyzing item-based databases. The Apriori-framework was developed for analyzing categorical data. However, many data include numerical values. Therefore, most existing techniques transform numerical values to categorical values. The transformation is done such that the rules are optimal with respect to support or confidence.