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A comprehensive survey of multi-objective genetic and fuzzy approaches in rule mining problem of knowledge discovery in databases

In this paper, we present a comprehensive survey on the multi-objective genetic-fuzzy approaches used in rule mining. While making this rigorous survey, we reveal that classification, association, and associative classification (integration of classification and association) rule mining are popularly used rule mining techniques in knowledge discovery in databases (KDD) for harvesting knowledge in the form of rule. The classical rule mining techniques based on crisp sets have bad experiences 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 quantifying the quality of these discovered rules. However, most of these objectives/criteria are in conflict with each other. Thus, fuzzy rule mining problems are modelled as multi-objective optimisation problem 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. Additionally, our survey highlights some popular state-of-the-art application areas of these approaches. Some future researches are given with an extensive list of relevant reference to make this area vibrant and active.