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...

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming pr...

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...

-There are various clustering models introduced for unsupervised learning. PFCM or the possibilistic c-means model was proposed in 2005. PFCM produces mainly three values: the typicality values, membership values and the centres of the clusters. It is a hybrid model of PCM and FCM. We propose an extension to PFCM so that it can be used to cluster the text files. Keywords— possibilistic model, f...

In 1987 Dubois and Prade defined an interval-valued expectation of fuzzy numbers, viewing them as consonant random sets. In 2001 Carlsson and Fullér defined an interval-valued mean value of fuzzy numbers, viewing them as possibility distributions, and they introduced the notation of crisp possibilistic mean value and crisp possibilistic variance of continuous possibility distributions, which ar...

This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean, variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. T...

Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provide better performance than Type-I Fuzzy sets, es...

Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves noise sensitivity problem Fuzzy (FCM) and coincident clusters possibilistic (PCM). Though PFCM a highly reliable but efficiency can be further improved by introducing concept suppression. Suppression-based algorithms employ winner non-winner based suppression technique on datasets, helping in perf...