Geodesic
Improvement of clustering by modification of degrees of fuzziness
Nečetkie sistemy i mâgkie vyčisleniâ, Tome 11 (2016) no. 2, pp. 95-101.

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Due to fast growth in technology, conventional classification methods are limited in their ability to support medical diagnostics without introducing considerable ambiguities. Since the conditions are vague in medicine the fuzzy methods may be more helpful rather than crisp ones. Classification depends on number of attributes, number of clusters to be classified and index of the clustering algorithm. Because it is not possible to reduce number of attributes and clusters, therefore changing the index value is a choice to improve performance. The objective of this paper is to analyze the improvement in terms of number of iterations taken, algorithm performance and percentage of correctness of Thyroid samples and wine samples classification by modifying the index of the algorithm.
Keywords: classification, fuzzy clustering, fuzzy c-means, index. 
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B. Venkataramana; L. Padmasree; M. Srinivasa Rao; D. Latha; G. Ganesan. Improvement of clustering by modification of degrees of fuzziness. Nečetkie sistemy i mâgkie vyčisleniâ, Tome 11 (2016) no. 2, pp. 95-101. http://geodesic.mathdoc.fr/item/FSSC_2016_11_2_a1/

[1] Bezdek J. C., Pattern Recognition with Fuzzy Objective Function Algorithms, Advanced Applications in Pattern Recognition, Springer, US, 1981 | DOI | MR

[2] Norman J., Your thyroid gland, EndocrineWeb http://www.endocrineweb.com/thyroid.html

[3] Broeckaert C. I., Jonckheer M., Massart D. L., “Comparison of multivariate discrimination techniques for clinical data – application to the thyroid functional state”, Methods of Information in Medicine, 22 (1983), 93–101

[4] Forina M., Aeberhard S., Leardi R., Wine Data Set, UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Wine

[5] Bezdek J. C., Ehrlich R., Full W., “FCM: The fuzzy c-means clustering algorithm”, Computers Geosciences, 10:2-3 (1984), 191–203 | DOI