Relative Unsupervised Discretization for Association Rule Mining

The paper describes a new, context-sensitive discretization algorithm that can be used to completely discretize a numeric or mixed numeric-categorical dataset. The method combines aspects of unsupervised (class-blind) and supervised methods. The central idea in the algorithm is what might be called "mutual structure projection" between the (numeric or categorical) attributes. The goal is to discretize a numeric attribute into intervals that correlate as much as possible with patterns in the value distributions of the other attributes. This is achieved by finding points of distribution changes, mapping them onto the target attribute, and subsequently clustering these points; the result is a set of significant split points that define the interval boundaries of the attribute discretization. This process can be performed for each numeric attribute in a dataset, thereby producing discretizations that reect potentially complex interrelationships among dierent attributes of the dataset. The algorith