Introduction to Knowledge Discovery in Medical Databases and Use of Reliability Analysis in Data Mining

• Autorzy: Elena Zaitseva , Miroslav Miroslav Kvassay , Vitaly Levashenko , Jozef Kostolny
• Języki publikacji: EN

Abstrakty

EN

Data mining (DM) is a collection of algorithms that are used to find some novel, useful and interesting knowledge in databases. DM algorithms are based on applied fields of mathematics and informatics, such as mathematical statistics, probability theory, information theory, neural networks. Some methods of these fields can be used to find hidden relation between data, what can be used to create models that predict some behavior or describe some common properties of analyzed objects. In this paper, we combine methods of DM with tools of reliability analysis to investigate importance of individual database attributes. Results of such investigation can be used in database optimization because it allows identifying attributes that are not important for purposes for which the database is used. Our approach is based on some coincidence between the key terms of DM and reliability analysis. (original abstract)

Słowa kluczowe

EN

Data analysis; Algorithms

PL

Analiza danych; Algorytmy

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 311--320

Twórcy

autor

Elena Zaitseva

• University of Zilina, Slovakia

autor

Miroslav Miroslav Kvassay

• University of Zilina, Slovakia

autor

Vitaly Levashenko

• University of Zilina, Slovakia

autor

Jozef Kostolny

• University of Zilina, Slovakia

Bibliografia

• W. J. Frawley, G. Piatetsky-Shapiro and C. J. Matheus, "Knowledge discovery in databases: An overview," in Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. J. Frawley, Eds. Cambridge, MA: AAAI/MIT Press, 1-27, 1991.
• V. Sigillito, "Pima Indians Diabetes Database," UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets/Pima Indians Diabetes]. Phoenix, AZ: National Institute of Diabetes and Digestive and Kidney Diseases, 1990.
• K. J. Cios and G. W. Moore, "Medical data mining and knowledge discovery: Overview of key issues," in Medical Data Mining and Knowledge Discovery, K. J. Cios, Ed. New York, NY: Physica Verlag Heidelberg, 2001, pp. 1-20.
• K. J. Cios, A. Teresinska, S. Konieczna, J. Potocka and S. Sharma, "A knowledge discovery approach to diagnosing myocardial perfusion," IEEE Engineering in Medicine and Biology Magazine, vol. 19, no. 4, pp. 17-25, Jul.-Aug. 2000.
• A. Petrie and C. Sabin, Medical Statistics at a Glance, 2nd ed. Oxford, UK: Blackwell Publishing Ltd, 2005.
• L. J. Cao, K. S. Chua, W. K. Chong, H. P. Lee and Q. M. Gu, "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine," Neurocomputing, vol. 55, no. 1-2, pp. 321- 336, Sep. 2003.
• O. Maimon and L. Rokach, "Introduction to knowledge discovery in databases," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. New York, NY: Springer Science+Business Media, Inc., 2005, pp. 1-17.
• M. Rausand and A. Høyland, System Reliability Theory: Models, Statistical Methods, and Applications. Haboken, NJ: John Wiley & Sons, Inc., 2004, 664 p.
• E. N. Zaitseva and V. G. Levashenko, "Importance analysis by logical differential calculus," Automation and Remote Control, vol. 74, no. 2, pp. 171-182, Feb. 2013, http://dx.doi.org/10.1134/ S000511791302001X.
• Y. Watanabe, T. Oikawa, and K. Muramatsu, "Development of the DQFM method to consider the effect of correlation of component failures in seismic PSA of nuclear power plant," Reliability Engineering & System Safety, vol. 79, no. 3, pp. 265-279, Mar. 2003, http://dx.doi.org/10.1016/S0951-8320(02)00053-4.
• B. Nystrom, L. Austrin, N. Ankarback, and E. Nilsson, "Fault tree analysis of an aircraft electric power supply system to electrical actuators," in Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on, 2006, pp. 1-7, http://dx.doi.org/10.1109/PMAPS.2006.360325.
• W.-C. Yeh, "A simple approach to search for all d-MCs of a limitedflow network," Reliability Engineering & System Safety, vol. 71, no. 1, pp. 15-19, Jan. 2001, http://dx.doi.org/10.1016/S0951- 8320(00)00070-3.
• E. Zaitseva, V. Levashenko, and M. Rusin, "Reliability analysis of healthcare system," in 2011 Federated Conference on Computer Science and Information Systems, FedCSIS 2011, 2011, pp. 169-175.
• B. Natvig, Multistate Systems Reliability Theory with Applications. New York, NY: Wiley, 2011, 262 p., http://dx.doi.org/10.1002/ 9780470977088.
• A. Lisnianski, I. Frenkel and Y. Ding, Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers. London, UK: Springer-Verlag London Ltd., 2010, 393 p., http://dx.doi.org/10.1007/978-1-84996-320-6.
• E. Zaitseva and V. Levashenko, "Multiple-valued logic mathematical approaches for multi-state system reliability analysis," Journal of Applied Logic, vol. 11, no. 3, pp. 350-362, Sep. 2013, http://dx.doi.org/10.1016/j.jal.2013.05.005.
• V. Levashenko and E. Zaitseva, "Fuzzy decision trees in medical decision making support system" in 2012 Federated Conference on Computer Science and Information Systems, FedCSIS 2012, 2012, pp. 213-219.
• J. D. Andrews and S. Beeson, "Birnbaum's measure of component importance for noncoherent systems," IEEE Transactions on Reliability, vol. 52, no. 2, pp. 213-219, Jun. 2003, http://dx.doi.org/10.1109/TR.2003.809656.
• E. Zaitseva, M. Kvassay, V. Levashenko, and J. Kostolny, "Reliability analysis of logic network by logical differential calculus," in 2014 ELEKTRO, 2014, pp. 245-250, http://dx.doi.org/10.1109/ ELEKTRO.2014.6848895.
• S. J. Upadhyaya and H. Pham, "Analysis of noncoherent systems and an architecture for the computation of the system reliability," IEEE Transactions on Computers, vol. 42, no. 4, pp. 484-493, Apr. 1993, http://dx.doi.org/10.1109/12.214699.
• W. Kuo and X. Zhu, Importance Measures in Reliability, Risk, and Optimization. Chichester, UK: John Wiley & Sons, Ltd, 2012, 472 p., http://dx.doi.org/10.1002/9781118314593.
• Z. W. Birnbaum, "On the importance of different components in a multicomponent system," in Multivariate Analysis, vol. 2, P. R. Krishnaiah, Ed. New York, NY: Academic Press, 1969, pp. 581-592.
• D. A. Butler, "A complete importance ranking for components of binary coherent systems, with extensions to multi-state systems," Naval Research Logistics Quarterly, vol. 26, no. 4, pp. 565-578, Dec. 1979, http://dx.doi.org/10.1002/nav.3800260402.
• W. S. Griffith, "Multistate reliability models," Journal of Applied Probability, vol. 17, no. 3, pp. 735-744, Sep. 1980, http://dx.doi.org/10.2307/3212967.
• S. Wu, "Joint importance of multistate systems," Computers & Industrial Engineering, vol. 49, no. 1, pp. 63-75, Aug. 2005, http://dx.doi.org/10.1016/j.cie.2005.02.001.
• J. Kostolny, M. Kvassay, and S. Kovalik, "Reliability analysis of noncoherent systems by logical differential calculus and binary decision diagrams," Komunikacie, vol. 16, no. 1, pp. 114-120, 2014.
• S. N. Yanushkevich, D. M. Miller, V. P. Shmerko and R. S. Stankovic, Decision Diagram Techniques for Micro- and Nanoelectronic Design. Handbook. Boca Raton, FL: CRC Press, 2006, 952 p.

Identyfikatory

DOI

10.15439/2015F327