Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

Clustering is a standard approach in analysis of data and construction of separated similar groups. The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering. The prominent feature of soft clustering leads to combine the rough and fuzzy sets. The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuz...

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome th...

This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modi3ed dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and ...

--Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful w...

A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters...

Based on a new hesitant fuzzy partial ordering proposed by Garmendia et al.~cite{GaCa:Pohfs}, in this paper a fuzzy disjunction ${D}$ on the set ${H}$ of finite and nonempty subsets of the unit interval and a t-conorm ${S}$ on the set $bar{{B}}$ of equivalence class on the set of finite bags of unit interval based on this partial ordering are introduced respectively. Then, hesitant fuzzy negati...