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Venkatesan, P.
- Clustering of Disease Data Base using Self Organizing Maps and Logical Inferences
Abstract Views :603 |
PDF Views:340
Authors
P. Venkatesan
1,
M. Mullai
2
Affiliations
1 Department of Statistics, NIRT(ICMR), Chennai, IN
2 Department of Mathematics, Ethiraj College For Women, Chennai, IN
1 Department of Statistics, NIRT(ICMR), Chennai, IN
2 Department of Mathematics, Ethiraj College For Women, Chennai, IN
Source
Indian Journal of Automation and Artificial Intelligence, Vol 1, No 1 (2013), Pagination: 2-6Abstract
Disease classification requires an expertise in handling the uncertainty. ANNs emerge as a powerful tool in this regard. ANNs have featured in a wide range of applications with promising results in biomedical sciences. The self-organized maps (SOM) use unsupervised learning to produce low dimensional discretized representation of the input space. SOMs are different from other neural networks in the sense that they use neighborhood function to preserve the topological properties of the input space. This paper compares Kohanen's SOM network with other clustering method. The SOM gives faster and accurate results in clustering the data. The results were presented and compared.Keywords
Medical Diagnosis, Artificial Intelligence (AI), Neural Network, Self Organizing Map(SOM), Best Matching Unit(BMU),Tuberculosis (TB)Full Text
References
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- Kohonen, T. (1982) Self-organizing formation of topologically correct feature maps, Biological Cyberbetics. Volume 43, 1982, pp:59-96
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- Victor Alves, Paulo Novais, Luis Nelas, Moreira Maia & Victor Ribeiro (2003) Case based reasoning versus artificial neural networks in medical diagnosis. Proceedings of IASTED International Conference Artificial Intelligence and Applications. pp: 1-5
- Rough Set Theory Approach for Attribute Reduction
Abstract Views :586 |
PDF Views:375
Authors
Affiliations
1 Department of Mathematics, Meenakshi College for Women, Chennai-24, IN
2 Department of Statistics, National Institute for Research in Tuberculosis, ICMR, Chennai-31, IN
1 Department of Mathematics, Meenakshi College for Women, Chennai-24, IN
2 Department of Statistics, National Institute for Research in Tuberculosis, ICMR, Chennai-31, IN
Source
Indian Journal of Automation and Artificial Intelligence, Vol 1, No 3 (2013), Pagination: 70-80Abstract
Knowledge Discovery from databases is practically important in many fields , including the field of medicine. Many methods are being developed for knowledge discovery and due to the availability of enormous amount of data, extraction of knowledge from database has become a challenging task. Researchers have proved methods, among which Rough Set Theory is an effective tool for knowledge discovery. In this paper, Rough Set Theory and its basic ideas are reviewed and applied to identify symptoms for diagnosing diabetes. This study also presents methods for extension to high dimensional data.in the medical domain.Keywords
Knowledge Discovery, Rough Set Theory, Discernibility Matrix, Reduct, Rule ExtractionFull Text
References
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- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996) From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
- Jensen, R., & Shen, Q. (2004) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. Knowledge and Data Engineering, IEEE Transactions on, 16(12), 1457-1471.
- Kalyani, P., & Karnan, M. (2011) A new implementation of attribute reduction using quick relative reduct algorithm. International Journal of Internet Computing, 1(1), 99-102.
- Komorowski, J., Pawlak, Z., Polkowski, L., & Skowron, A. (1999) Rough sets: A tutorial. Rough fuzzy hybridization: A new trend in decision-making, 3-98.
- Magnani, M. (2003) Technical report on rough set theory for knowledge discovery in data bases. Bologna, Italy: University of Bologna.
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- Xu, Y., Cao, Y., & Yang, S. (2011). Research on Care of Postoperative Patient based on Rough Sets Theory. International Journal of Computer Applications,31(10).
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