Data mining stages and its crucial concepts

The term data mining refers to the analysis of large observational data sets with the goal of finding unsuspected relationships. A data set can be “large” either in the sense that it contains a large number of records or that a large number of variables is measured on each record. Data Mining as an analytic process designed to explore data in search for 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 most direct business applications. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in today's business environment, efficiency or speed is not the only key for competitiveness. These types of huge amount of data's are available in the form of tera- to peta-bytes which has drastically changed in the areas of science and engineering. We need techniques called the data mining which will transform in many fields. In This paper we discuss the concept of data mining Stages, and some crucial concepts in it like stacked generalization, voting, Feature Selection, Bagging etc.