Approximate Boolean Reasoning: Foundations and Applications in Data Mining

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Abstract

Since its introduction by George Boole during the mid-1800s, Boolean algebra has become an important part of the lingua franca of mathematics, science, engineering, and research in artificial intelligence, machine learning and data mining. The Boolean reasoning approach has manifestly become a powerful tool for designing effective and accurate solutions for many problems in decision-making and approximate reasoning optimization. In recent years, Boolean reasoning has become a recognized technique for developing many interesting concept approximation methods in rough set theory. The problem considered in this paper is the creation of a general framework for concept approximation. The need for such a general framework arises in machine learning and data mining. This paper presents a solution to this problem by introducing a general framework for concept approximation which combines rough set theory, Boolean reasoning methodology and data mining. This general framework for approximate reasoning is called Rough Sets and Approximate Boolean Reasoning (RSABR). The contribution of this paper is the presentation of the theoretical foundation of RSABR as well as its application in solving many data mining problems and knowledge discovery in databases (KDD) such as feature selection, feature extraction, data preprocessing, classification of decision rules and decision trees, association analysis.