Applying Variable Precision Rough Set for Clustering Diabetics Dataset

• Main Authors: Herawan, Tutut, Wan Maseri, Wan Mohd, Noraziah, Ahmad
• Format: Article
• Language: English
• Published: SERSC 2014
• Subjects: QA Mathematics
• Online Access: http://umpir.ump.edu.my/id/eprint/3788/1/2013_maseri_Applying.pdf; http://umpir.ump.edu.my/id/eprint/3788/; http://www.sersc.org/journals/IJMUE/vol9_no1_2014/21.pdf

Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a rough-set based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute.