Miin-Shen Yang

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In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the in1uence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means(More)
This paper presents a new method for similarity measures between intuitionistic fuzzy sets (IFSs). We will present a method to calculate the distance between IFSs on the basis of the Hausdorff distance. We will then use this distance to generate a new similarity measure to calculate the degree of similarity between IFSs. Finally we will prove some(More)
In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. Since the FCM memberships do not always explain the degrees of belonging for the data well, Krishnapuram and Keller proposed a possibilistic approach to clustering to correct this weakness of FCM. However, the performance of Krishnapuram and Keller’s(More)
The importance of suitable distance measures between intuitionistic fuzzy sets (IFSs) arises because of the role they play in the inference problem. A concept closely related to one of distance measures is a divergence measure based on the idea of information-theoretic entropy that was first introduced in communication theory by Shannon (1949). It is known(More)
Classification of objects is an important area in a variety of fields and applications. In the presence of full knowledge of the underlying joint distributions, Bayes analysis yields an optimal decision procedure and produces optimal error rates. Many different methods are available to make a decision in those cases where information of the underlying joint(More)
In this paper, we first review several popular similarity measures between fuzzy sets and then extend those similarity measures to intuitionistic fuzzy sets. We also propose two new similarity measures between intuitionistic fuzzy sets. These similarity measures have been found to satisfy some similarity measure axioms. Several numerical experiments are(More)
This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised(More)