Rough sets is a fairly new and promising technique for data mining and knowledge discovery from databases. This tutorial article presents the fundamentals of rough set theory in a nontechnical manner and outlines how the technique can be used to extract minimal if-then rules from tables of empirical data that either fully or approximately describe given example classifications. An example application for prediction of ambulation for patients with spinal cord injury is given. Because such rules are readily interpretable, they can be inspected to yield possible new insight into how various contributing factors interact and, thus, serve as hypothesis generators for further research. Additionally, the set of mined rules may function as a classifier of new, unseen cases.
From the Knowledge Systems Group, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (AØ), and Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts (TR).
All correspondence and requests for reprints should be addressed to Todd Rowland, MD, 700 S. College Avenue, Suite A, Bloomington, IN 47403.
Supported, in part, by grant 74467/410 from the Norwegian Research Council, grant H133N50015 from the National Institute on Disability and Rehabilitation Research, and grant R55LM/OD6538-01 from the National Library of Medicine.