Clustering algorithm for classification methods

• Autorzy: Łęski J. , Jeżewski M.
• Języki publikacji: EN

Abstrakty

EN

Classification plays an important role in many fields of life, including medical diagnosis support. In the paper, fuzzy clustering algorithm dedicated to classification methods is proposed. Its goal is to find pairs of prototypes located near boundaries of both classes of objects. The minimization procedure of the proposed criterion function is described. The algorithm for determining the value of the clustering parameter is also presented. Presented results (synthetic dataset) confirm correctness of clustering - most of final prototypes, determined based on obtained pairs, are located between boundary of two classes.

Słowa kluczowe

EN

clustering; classification; class boundaries

PL

grupowanie; klasyfikacja

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 20
• Strony: 11--18
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Łęski J.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

autor

Jeżewski M.

Bibliografia

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• [5] JEZEWSKI M., An application of modified fuzzy clustering to medical data classification, Journal of Medical Informatics and Technologies, 2011, Vol. 17, pp. 51-57.
• [6] JEZEWSKI M, CZABANSKI R., WROBEL J., HOROBA K., Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome, Biocybernetics and Biomedical Engineering, 2010, Vol. 30(4), pp. 29-47.
• [7] LESKI J., An ε-margin nonlinear classifier based on if-then rules, IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics, 2004, Vol. 34, No. 1, pp. 68-76.
• [8] LESKI J., Neuro-fuzzy systems, WNT, Warsaw, 2008, (in Polish).
• [9] LESKI J., JEZEWSKI M., Fuzzy clustering with prototype pairs, X Jubilee Scientific Seminary “Selected problems of electrotechnology and electronics” (WZEE 2012), (submitted paper).
• [10] OGIELA M.R., TADEUSIEWICZ R., Pattern recognition, clustering and classification applied to selected medical images, Studies in Computational Intelligence, Vol. 84, 2007, Springer-Verlag, Berlin Heidelberg.
• [11] PANDEY B., MISHRA R.B., Knowledge and intelligent computing system in medicine, Computers in Biology and Medicine, 2009, Vol. 39, pp. 215-230.