In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or ...

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

Possibilistic logic is a weighted logic introduced and developed since the mid-1980s, in the setting of arti(cial intelligence, with a view to develop a simple and rigorous approach to automated reasoning from uncertain or prioritized incomplete information. Standard possibilistic logic expressions are classical logic formulas associated with weights, interpreted in the framework of possibility...

Applying traditional clustering techniques to big data on the cloud while preserving privacy of is a challenge due required division and exponential operations in each iteration, which complicate its implementation encrypted data. Several existing approaches are based approximating formulas centers, weights, memberships as three polynomial functions according multivariate Taylor formula. Howeve...

An architecture for the implementation of possibilistic models in an object-oriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilistic processes are speciied. Supplementary methods|including the fast MM obius transform, the maximum...

In this paper, we analyzed the segmentation of MRI brain image into different tissue types on brain image using Possibilistic fuzzy c-means (PFCM) clustering. Application of this method to MRI brain image gives the better segmentation result in compare with Fuzzy c-mean (FCM) and fuzzy possibilistic c-means (FPCM). The results are verified quantitatively using similarity metrics, false positive...

In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. R...

Intention recognition has significant applications in ambient intelligence, assisted living and care of the elderly, games and intrusion and other crime detection. In this chapter we explore an approach to intention recognition based on clustering. To this end we show how to map the intention recognition problem into a clustering problem. We then use three different clustering algorithms, Fuzzy...

In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining optimal number clusters. This paper presents a new index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP (index, which works well clusters that vary shape density. Moreover, FPCM like most algorithms is susceptible ini...