Fuzzy c-means (FCM) is an important clustering algorithm. However, it does not consider the impact of different feature on clustering. In this paper, we present a fuzzy clustering algorithm with the generalized entropy of feature weights FCM (GEWFCM). By introducing feature weights and adding regularized term of their generalized entropy, a new objective function is proposed in terms of objecti...

A new fuzzy technique called fuzzy entropy (FE) clustering is proposed and applied to hidden Markov models (HMMs) for speech recognition. FE-HMMs, both discrete and continuous, are proposed in this paper. Experimental results in speech recognition show good results for FE models compared with fuzzy C-means and conventional models.

We consider information-theoretic key agreement between two parties sharing somewhat different versions of a secret w that has relatively little entropy. Such key agreement, also known as information reconciliation and privacy amplification over unsecured channels, was shown to be theoretically feasible by Renner and Wolf (Eurocrypt 2004), although no protocol that runs in polynomial time was d...

The main aim of this article is to revisit the definitions of subsethood measure which are predomonant in the literature of fuzzy set theory. It is also intended to revisit the relationship between fuzzy entropy and conditioning as proposed by some authors. We wish to analyze these with the extended definition of complementation of fuzzy sets on the basis of reference function. The existing def...

In this paper, we proposed an algorithm to find the optimal threshold value for denoising an image. A new cost function is designed to find the optimal threshold in every image. The cost function is based on the intuitionistic fuzzy divergence measure of the denoised image and original image. In addition, the intuitionistic fuzzy entropy of denoised image is added to the cost function. This is ...

We propose a new measure of entropy for intuitionistic fuzzy sets. This paper is in a sense a continuation of our previous paper on entropy of intuitionistic fuzzy sets — the inferences are based on the same two types of distances as previously — to the farer and to the nearer crisp elements. But now we propose the formulas easier to calculate. Next, we show that the same result can be obtained...

Let X be a compact metric space and let f : X → X be continuous. It is well known that a given discrete dynamical system (X, f) induces a map Φ on the space F(X) of fuzzy sets. In this paper we study relations between sizes of topological entropies of a given crisp dynamical system (X, f) and its fuzzy counterpart (F(X),Φ). Among another things, we present a constructive proof of the fact that ...

Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems. Kernel methods map data into a higher dimensional feature space, where the resulting structure of the classification task is linearly separable; while rough sets granulate the universe with the use of relations and employ the induced knowledge granules to approximate arbitrary conce...