© Serials Publications ISSN 0973-7448 APPLICATION OF FUZZY DATA MINING IN THE PREDICTION OF BUSINESS TRENDS Jugendra Dongre 1 , G L Prajapati 2 and S. V. Tokekar 2 1 International Institute of Professional Study, Devi Ahilya University, Indore (M. P.) India 2 Institute of Engineering and Technology, Devi Ahilya University, Indore (M. P.) India E-mails: jugendra_kumar@rediffmail.com, glprajapati1@gmail.com, sanjivtokekar@yahoo.com Abstract: As far as the study of manpower resources is increasing, driving useful information and helpful knowledge from databases are evolving into an important research area. Knowledge discovery in databases plays an important role in finding the future trends in retail sector. In this paper, we use fuzzy data mining approach for predicting business trends. The concept of fuzzy logic in data mining may leads to a better result as compared to crisp logic. This work also analyzes the customer historical data to evaluate the customers’ trends. According to the customer behavior we apply fuzzy computing in terms of fuzzy rules through managing quantitative data to infer more general and important knowledge from data. Keywords: Data Mining, Fuzzy Data Mining, Knowledge Discovery in Databeses, Fuzzy Logic I J C S S A Vol. 6, No. 2, July-December 2012, pp. 115-119 1. INTRODUCTION Data mining has useful business applications such as finding useful hidden information from databases, predicting future trends, and making good business decisions [1] [4] [5]. Soft computing techniques such as fuzzy logic, rough sets, and neural networks are useful in data mining [l] [7]. Granular computing techniques [2] [3] [9] can be used in data mining applications [2] [3] [6] [8]. Data Mining is the process of discovering new correlations, patterns, and trends by digging into large amounts of data stored in warehouses, using artificial intelligence like fuzzy logic. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data. The alternative name of Data Mining is Knowledge discovery in databases (KDD), knowledge extraction, data/pattern analysis, etc. The importance of collecting data that reflect the business. In this study, we use the historical data of customers and then feedback it to decision making for finding the future trends for batter business. From the association rule of fuzzy data mining we can know what the most customers prefer. Increased competition, demanding customers, and flat sales are just a few of the challenges faced by the retail industry. These are compounded with the fact that many retailers don’t have an in-depth understanding of customer behavior and buying habits, which are key factors in determining decisions on Product, Price, Promotion and Place. To be successful in this environment, retailers have to develop a good knowledge about their profitable customers and customer segments, and devise effective strategies to acquire, retain and grow customer value. We provide the retail industry a comprehensive suite of integrated knowledge discovery, predictive modeling, and strategy development software programs that are easy to deploy, learn and use, and that drive significant return on investment within weeks of deployment. Our predictive analytics systems empower retail marketing, sales and risk management organizations to create revenue opportunities, and reduce risks of loss, at every stage of the customer life cycle from acquisition and targeting, through providing the insight to develop products that are based on a thorough understanding of the target customer segments, to effectively targeting the right customers, and determining the right store locations. 2. RELATED WORK 2.1. Fuzzy Logic Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership.