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Applying Variable Precision Rough Set for Clustering Diabetics Dataset

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Variable Precision Rough Set
  2.1. Set Approximations
  2.2. Variable Precision Rough Set
 3. Rough Set-based Techniques for Selecting a Clustering Attribute
 4. Experiment Results
  4.1. Material
  4.2. Clustering problem
  4.3. Result
  4.4. Cluster purity and its visualization
 5. Conclusion
 Acknowledgements
 References

저자정보

  • Tutut Herawan Department of Mathematics Education, Universitas Ahmad Dahlan Jalan Prof Dr Soepomo 55166, Yogyakarta, Indonesia
  • Wan Maseri Wan Mohd Faculty of Computer System and Software Engineering Universiti Malaysia Pahang Lebuh Raya Tun Razak, Gambang 26300, Kuantan, Pahang, Malaysia

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