Optimization of Automation in Fuzzy Decision Rules

The advances in data collection have generated an urgent need for techniques that can intelligently and automatically analyze and mine knowledge from huge amounts of data. The Knowledge Discovery in Databases (KDD) is the process of extracting the knowledge from huge data collection. Data mining is a step of KDD in which patterns or models are extracted from data by using some automated techniques. Discovering knowledge in the form of classification rules is one of the most important tasks of data mining. Discovery of comprehensible, concise and interesting rules helps us to make right decisions. Therefore, several Machine Learning techniques such as Neural Network, Decision Tree Induction, K nearest neighbour and Bayesian approach have been applied for automated discovery of classification rules. Recently there have been several applications of genetic algorithms for successful discovery of concise and comprehensible rules with high predictive accuracy.