An Evolutionary Data Mining Model for Fuzzy Concept Extraction

Considering the fast growth of data contents in terms of size as well as variety, finding useful information from collections of data have been extensively investigated in the past decade. In this paper a method is proposed for extracting useful information from a relational database using a hybrid of genetic algorithm and fuzzy data mining approach to extract user desired information. The genetic algorithm is employed to find a compact set of useful fuzzy concepts with a good fuzzy support for the output of fuzzy data mining process. Experimental results show superiority of the proposed evolutionary system as compared to the common fuzzy grid-based data mining.