Identification of Thyroid Disease Severity using Fuzzy C-Means

G. Jaya Suma, M. Nymisha, Sk. Teenaaz, S. Neelima, K. Gouri Shankar


Clustering is a primary data description method in data mining which group’s most similar data. Data clustering is one of the important problems in the fields of data mining, bio-informatics and pattern recognition. Various algorithms are used to solve this problem. This paper presents the performance analysis of k-means clustering algorithm and compares it with fuzzy C- Means (FCM) algorithm on Thyroid disease data set. We also find the accuracy of both the algorithms on Thyroid data set. Fuzzy clustering is useful to mine multi-dimensional and complex data sets, where the members have fuzzy or partial relations. Among the various techniques developed so far, fuzzy-C-Means (FCM) algorithm is the most popular one, where a partial membership is assigned to a piece of data with each of the pre-defined cluster centers. Moreover, in FCM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. With in some iteration the cluster centers and membership values of the data points are updated. The FCM employs fuzzy portioning such that a point can belong to more than one group with different membership grades between 0 and 1. Using this membership we are going to determine the severity of thyroid disease in a person.

Keywords: K-means, fuzzy C-means clustering, Euclidean distance, Manhattan distance, sigmoid membership, t-norms

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Gan G., Ma C., Wu J. Data clustering: Theory, algorithms, and applications. ASA-SIAM Series on Statistics and Applied Probability.

McQueen J. B. Some methods of classification and analysis of multivariate observations. Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley. 1967; 281–297p.

Wang, X. Y., Whitwell, G., Garibaldi, J. The Application of a Simulated Annealing Fuzzy Clustering Algorithm for Cancer Diagnosis. Proceedings of the IEEE 4th International Conference on Intelligent System Design and Application, Hungary. 2004; 467–472.

Wang, X. Y., Garibaldi, J., Ozen, T. Application of The Fuzzy C-Means Clustering Method on the Analysis of non Pre-processed FTIR Data for Cancer Diagnosis. Proceedings of the 8th Australian and New Zealand Conference on Intelligent Information Systems Australia. 2003; 10(12): 233–238p.

Frigui H., Krishnapuram R. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 1999; 21: 450–465p.

Yanp M. S., Wu K.L., Yub J. A novel fuzzy clustering algorithm. IEEE International Symposium on Computational Intelligence in Robotics and Automation. 2003; 2: 647–652p.

Mantsch,M., Jackson M. Spectroscopy in biodiagnostics (From Hippocrates to Herschel and beyond). Journal of Molecular Structure. 1995; 347: 187–206p.

Almeida R. J., Sousa J. M. C. Comparison of fuzzy clustering algorithms for Classification. International Symposium on Evolving Fuzzy Systems. 2006; 112–117p.

Yu J., Yang M.S. Optimality test for generalized FCM and its application toparameter selection. IEEE Trans. Fuzzy Syst. 2005; 13: 164–176p.

Prodip Hore, Lawrence O. Hall, Dmitry B. Goldgof. Single Pass Fuzzy C Means. CSEEE.2000; 28.

2007Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press.1981.

472pYu J. General c-means clustering model. IEEE Trans. Pattern Anal. Mach. Intel. 2005; 27:1197–1211p.

238pLeski J. Towards a robust fuzzy clustering. Fuzzy Sets Syst. 2003; 137(2003): 215–233p.

Yang M.S. A survey of fuzzy clustering. Math. Computer. Modelling; 18(1993): 1–16p.


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