In this paper, fuzzy quantitative models of language statistics are constructed. All suggested models are based on the assumption about a superposition of two kinds of uncertainties: probabilistic and possibilistic. The realization of this superposition in statistical distributions is achieved by the probability measure splitting procedure. In this way, the fuzzy versions of generalized binomia...

Fuzzy inference systems are widely used for classification and control. They can be designed from the training data. This paper describes a technique for deriving fuzzy classification rules from the interval-valued data. The technique based on a heuristic method of possibilistic clustering and a special method of the interval-valued data preprocessing. Basic concepts of the heuristic method of ...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...

The present fuzzy arithmetic based on Zadeh’s possibilistic extension principle and on the classic definition of a fuzzy set has many essential drawbacks. Therefore its application to the solution of practical tasks is limited. In the paper a new definition of the fuzzy set is presented. The definition allows for a considerable fuzziness decrease in the number of arithmetic operations in compar...

Market risk exposure plays a key role for financial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incur when the price of the portfolio’s assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of financial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estima...

Market risk exposure plays a key role for financial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incur when the price of the portfolio’s assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of financial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estima...

Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the ‘standard’ Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate propertie...

Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanob...

Linear equality systems with fuzzy parameters and crisp variables defined by the extension principle are called possibilistic linear equality systems. The study focuses on the problem of stability (with respect to perturbations of fuzzy parameters) of the solution in these systems.

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, whic...