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Quantitative Association Rule Mining Using Information-Theoretic Approach

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Quantitative Association Rule Mining Using Information-Theoretic Approach

Mary Minge
University of Computer Studies, Lashio dimennyaung@gmail.com Abstract

Quantitative Association Rule (QAR) mining has been recognized an influential research problem due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike Boolean Association Rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. To develop a data mining system for huge database composed of numerical and categorical attributes, there exists necessary process to decide valid quantization of the numerical attributes. One of the main problems is to obtain interesting rules from continuous numeric attributes. In this paper, the Mutual Information between the attributes in a quantitative database is described and normalization on the Mutual Information to make it applicable in the context of QAR mining is devised. It deals with the problem of discretizing continuous data in order to discover a number of high confident association rules, which cover a high percentage of examples in the data set. Then a Mutual Information graph (MI graph), whose edges are attribute pairs that have normalized Mutual Information no less than a predefined information threshold is constructed. The cliques in the MI graph represent a majority of the frequent itemsets.

Keywords: Quantitative Databases, Association Rules, Mutual Information

1. Introduction

Association rule mining is a significant research topic in the knowledge discovery area. Association analysis is a useful data mining technique exploited in multiple application domains [6]. One of the best known is the business field where the discovering of purchase patterns or associations between products that clients...

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