Multi-Objective Genetic and Fuzzy Approaches in Rule Mining Problem of Knowledge Discovery in Databases

Abstract

Knowledge Discovery in Databases (KDD) is the process of automatically searching patterns from large volumes of data by using specific data mining techniques. Classification, association, and associative classification (integration of classification and association) rule mining are popularly used rule mining techniques in KDD for harvesting knowledge in the form of rule. The classical rule mining techniques based on crisp sets have bad experience of “sharp boundary problems” while mining rule from numerical data. Fuzzy rule mining approaches eliminate these problems and generate more human understandable rules. Several quality measures are used in order to quantify the quality of these discovered rules. However, most of these objectives/criteria are conflicting to each other. Thus, fuzzy rule mining problems are modeled as multi-objective optimization problems rather than single objective. Due to the ability of finding diverse trade-off solutions for several objectives in a single run, multi-objective genetic algorithms are popularly employed in rule mining. In this chapter, the authors discuss the multi-objective genetic-fuzzy approaches used in rule mining along with their advantages and disadvantages. In addition, some of the popular applications of these approaches are discussed.