Use of Rough Sets as a Data Mining Tool for Experimental Bio- Data

Document Type

Contribution to Book

Publication Date

6-1-2008

Publication Title

Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applicationjs

ISBN

978-3-540-70778-3

ISSN

1860-9503

Abstract

The Rough Sets methodology has great potential for mining experimental data. Since its introduction by Pawlak, it has received a lot of attention in the computing community. However, due to the mathematical nature of the Rough Sets methodology, many experimental scientists lacking sufficient mathematical background have been hesitant to use it. The goal of this chapter is twofold: (1) to introduce “Rough Sets” methodology (along with one of its derivatives, “Modified Rough Sets”) in a non-mathematical fashion hoping to share the potentials of this approach with a larger group of non-computationally-oriented scientists (Mining of one specific form of implicit data within a bio-dataset is also discussed), and (2) to apply this methodology to a dataset of children with and without Attention Deficit/Hyperactivity Disorder (ADHD), to demonstrate the usefulness of the approach in patient differentiation. Discriminant Analysis statistical approach as well as the ID3 approach were also applied to the same dataset for comparison purposes to find out which approach is most effective.

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