On the Potential of Numerical Association Rule Mining

Abstract

In association rule mining, both the classical algorithms and today’s available tools either use binary data items or discretized data. However, in real-world scenarios, data are available in many different forms (numerical, text) and these types of data items are not supported in the classical association rule mining algorithms. There are some association rule mining algorithms that have been proposed for numerical data items but unfortunately, for working data scientists and decision makers, it is challenging to find concrete algorithms that fit their purposes best. Therefore, it is highly desired to have a study on the different existing numerical association rule mining algorithms (NARM). In this paper, we provide such a detailed study by thoroughly reviewing 24 NARM algorithms from different categories (optimization, discretization, distribution).