Data mining versus Computational Statistics

To me, the concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty and recently, there has been increased interest in developing new analytic techniques specifically designed to address the issues relevant to business Data Mining like Classification Trees, but Data Mining is still based on the conceptual principles of statistics including the traditional Exploratory Data Analysis (EDA) and modeling and it shares with them both some components of its general approaches and specific techniques. And all this is not possible with just computaional statistics..

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 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.