Fuzzy rule extraction using hybrid evolutionary models for data mining systems

Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a data mining system for extracting knowledge from input datasets. We use the hybrid ant colony and simulated annealing algorithms to optimize extracted fuzzy rule set. The proposed method has the main feature of data mining techniques which is high accuracy. The proposed method is then implemented on UCI datasets. The results are compared with those of well-known methods, and show the competitive systems efficiency.