TY - CHAP
T1 - The use of rough sets as a data mining tool for experimental bio-data
AU - Hashemi, Ray R.
AU - Tyler, Alexander A.
AU - Bahrami, Azita A.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=59549099690&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-70778-3_3
DO - 10.1007/978-3-540-70778-3_3
M3 - Chapter
AN - SCOPUS:59549099690
SN - 9783540707769
T3 - Studies in Computational Intelligence
SP - 69
EP - 91
BT - Computational Intelligence in Biomedicine and Bioinformatics
A2 - Smolinski, Tomasz G.
A2 - Milanova, Mariofanna
A2 - Hassanien, Aboul-Ella
A2 - Hassanien, Aboul-Ella
ER -