Data Mining in Institutional Research

What is Data Mining?

The process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories and by using pattern recognition technologies as well as statistical and mathematical techniques (The Gartner Group).
The Nontrivial extraction of implicit, previously unknown and potentially useful information from data (Frawley, Paitestky-Shapiro and Mathews).

Data Analysis for institutional research (IR) has evolved from simple retrospective data delivery in the 1960's to retrospective dynamic data delivery at multiple levels in the 1990's. Unlike the past methodologies, data mining is prospective and proactive in data analysis and information delivery. With a blend of tools and techniques from disciplines such as statistics, computer science, mathematics, biology and engineering, data mining provides new opportunities for institutional research professionals to provide decision support data. This site provides a collection of resources from an introductory perspective for institutional research professionals interested in data mining.
Data mining has been used by universities in a number of areas, including but not limited to enrollment management, retention and graduation analysis, survey data analysis, and donation prediction (alumni contribution).
As this area is still in its infant stages, real world examples of IR applications are difficult to find. As more and more examples in IR become available, this site will be updated.