Cyber Times International Journal of Technology & Management

With the mounting growth of data in every application, data mining meets the imminent need for effective, scalable and flexible data analysis in our society. Data mining can be considered as a natural evolution of information technology and a confluence of several disciplinary and application domains. In the process of data mining, data is collected, analyzed and then seen from different perspectives. This data is summarized into productive information that is useful in increasing cuts costs or both. Data mining originates its name from the resemblancesamongtwo processes: searching for treasuredinformation (trend and patterns in data) in a large database and mining a mountain inveinof finding valuable ore. Both the processes involve either filtering an immense amount of material, or intelligently investigating it to find valuable product or information. Data mining tools interpret what will be the future tendency which helps in business to make important and profitable decision. In earlier times finding answer to these business questions was very time consuming. They examine database very minutely to find hidden patterns and predict information that earlier experts would miss as it lies outside their expectations.

Data mining is a practical topic and involves learning in a practical, not a theoretical sense. We are interested in techniques for finding and describing structural patterns in data as a tool for helping to explain the data and make predictions from it. This paper gives the review of data mining (evolution, process, practical applications, and challenges) and describes its major tools and techniques such as cluster analysis, artificial intelligence, decision tree approach, genetic algorithm, and visualization.