International Journal of Computer Applications (0975 – 8887) Volume 51– No.3, August 2012 13 Optimization of Decision Rules in Fuzzy Classification Renuka Arora M.tech student B.R.C.M. College of Engineering & Technology, Bahal (Bhiwani) ABSTRACT There are various advances in data collection that can intelligently and automatically analyze and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. 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 effective rules helps us to make right decisions. Therefore, several Machine Learning techniques are applied for discovery of classification rules. Recently there have been several applications of genetic algorithms for effective rules with high predictive accuracy. Keywords: Classification, Genetic Programming, Evolutionary Algorithms 1. INTRODUCTION In the last several decades, human capabilities of both generating and collecting data have increased rapidly. Contributing factors include the computerization of many business, scientific and government transactions, and advances in data collection tools ranging from scanned text and image platforms to satellite remote sensing systems. In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. To cope with such complexities people have generated an urgent need for new techniques and various tools that can intelligently assist them in transforming the vast amounts of data into useful information and knowledge. These intelligent systems provide an infrastructure that identifies hidden patterns in the gathered data which could discover useful knowledge from data and thereby empower managers to make more effective decisions. 2. KNOWLEDGE DISCOVERY IN DATABASE (KDD) KDD is the process of finding useful information and patterns in data. It starts with the understanding of the problem and concludes with the analysis and assessment of the results. The input to this process is the data, and the output is the useful information required by the user. Sudesh Kumar Phd, Associate Professor B.R.C.M. College of Engineering & Technology, Bahal (Bhiwani) KDD Process: The KDD process includes two steps: 1. Preprocessing [or Data Preparation] step The goal of data preparation methods is to transform the data to facilitate the application of given data mining algorithms. 2. Post processing [or Knowledge Refinement] step The goal of knowledge refinement methods is to validate and refine discovered knowledge. Fig. 1: The iterative nature of the knowledge discovery process The KDD process is both interactive and iterative, involving numerous steps with many decisions being made by the user. KDD is iterative because the output of each step is often feedback to previous steps as shown in Figure and typically many iterations of this process are necessary to extract high- quality knowledge from data. 3 DATA MINING TASKS Data mining tasks are also referred to as data mining outcomes or types and can be classified into two categories: descriptive and predictive. Descriptive mining tasks characterize general properties of the data in the database. Examples include association rule discovery and clustering. On the other hand, predictive mining tasks perform inference on the current data in order to make predictions. Examples of predictive mining tasks include classification and regression. In general, the main data mining methods includes: classification, regression, link analysis, segmentation and deviation detection. 3.1Classification Classification involves mapping data into one of several predefined or newly discovered classes. In the illustration shown in Fig. 2, there are three groups or classes of data, (A), (B), and (C). The classification rule may specify minimum Data Mini ng Knowledge Validation and Refinement [Post processing] Data Preparation [Preprocessing]