An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining

Abstract

Mining quantitative association rules is an important topic of data mining since most real world databases have both numerical and categorical attributes. Typical solutions involve partitioning each numerical attribute into a set of disjoint intervals, interpreting each interval as an item, and applying standard boolean association rule mining. Commonly used partitioning methods construct set of intervals that either have equal width or equal cardinality. We introduce an adaptive partitioning method based on repeatedly merging smaller intervals into larger ones. This method provides an effective compromise between the equal width and equal cardinality criteria. Experimental results show that the proposed method is an effective method and improves on both equal-width partitioning and equal-cardinality partitioning.