Principles Of Data Mining

The principles of data mining draw from a wide range of disciplines, including computer science and statistics. These principles include an understanding of how to store and organize data, how to access it most efficiently, and how to draw insights from analysis of the data. These principles have been applied to a large number of fields, from private industry to government organizations.

Manufacturers that make use of different facilities may want to examine how differences in their processes have affected the success and failure rates of their products. Retailers may use data mining techniques to classify their customers based on similar behavioral patterns. Service providers can create profiles of prospective customers based on limited information by comparing what they know about prospects to the information about existing customers.

Applying the Principles of Data Mining

Based on your organization's interest, you can act upon the principles of data mining to improve your operations:

• Once you have collected data, you can apply statistical modeling methods such as classification and forecasting.
• Many existing software packages include the tools that you need to perform data mining, as long as you understand the basic principles.
• While the underlying principles remain unchanged, techniques in data mining continue to be advanced with the use of neural networks and Bayesian models.

If you are interested in learning the principles of data mining, we at The Modeling Agency offer a series of seminars. Our introductory seminar provides you with an overview through the use of popular cases. We then drill down into the methods that may interest you and demonstrate how you can apply them to problems that you routinely encounter.