ABSTRACT
Data mining implies extraction of knowledge embedded in the data. In this age of large databases and data warehouses, where almost automatically, or with very little guidance, data is being captured continuously, preconceived notions about data structure hardly exist. Data mining is a process of exploration and analysis of large amounts of data in order to discover meaningful patterns. Data mining can also be considered as a business process for maximizing the value of data collected for the business. Data is always showing patterns; it is important to know which patterns are non-random and which among them are actionable. Data captured is always past data and it is valuable only when it can be used to understand its future behavior. The most important assumption is that the future will be similar to the past so that knowledge from the past is usable in the future. In order to do that, pattern recognition in past data is important. However, business is a continuous process and the system will be evolving continuously. To understand the changing nature of the business, continuous monitoring of the patterns in the data and recognition of their slow but steady evolution are important.
