Abstract: Data mining is a process which finds useful patterns from large amount of data The process of extracting previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions - Simoudis 1996 This data mining definition has business flavor and for business environments. However, data mining is a process that can be applied to any type of data ranging from weather forecasting, electric load prediction, product design, etc. Data mining also can be defined as the computer-aid process that digs and analyzes enormous sets of data and then extracting the knowledge or information out of it. By its simplest definition, data mining automates the detections of relevant patterns in database. Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions). In this paper, the main area of concentration was to optimize the rules that are generated by an Association Rule Mining algorithm (Eclat) by using a Genetic Algorithm. Here we generate more accurate and complete rules. The advantage of using genetic algorithm is to discover high level prediction rules.
Keywords: Eclat, Genetic Algorithm, Association Rule, Data Mining.