Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...

This paper presents new algorithms (Fuzzy c-Medoids or FCMdd and Robust Fuzzy c-Medoids or RFCMdd) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known Relational Fuzzy c-Means algorit...

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

In this paper, we study countably compact fuzzy sets using the definition of C. K. Wong [10] and obtain its several properties. 1. Introduction The concept of fuzzy set and fuzzy set operations were first introduced by L. A. Zadeh [11] in1965. Several other mathematicians studied fuzzy sets in various areas in mathematics. Firstly, C. L. Chang [2] in 1968 developed the theory of fuzzy topologic...

In recent years, the Fuzzy Relational Database and its queries have gradually become a new research topic. Fuzzy Structured Query Language (FSQL) is used to retrieve the data from fuzzy database because traditional Structured Query Language (SQL) is inefficient to handling uncertain and vague queries. The proposed model provides the facility for naïve users for retrieving relevant results of no...

in this study, we introduce and study a concept of distributed fuzzymodeling. fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. in contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. i...

In this work the importance of fuzzy based clustering methods is highlighted and their applications in the field of chemoinformatics, and issues involved are reviewed. The various methods and approaches of fuzzy clustering are outlined. The issue of number of valid clusters in a dataset is also discussed. The hyper dimensional chemical datasets are traditionally been treated only with the help ...

Abstract Financial institutions use credit rating models to make lending, investing, and risk management decisions. Credit have been developed using a variety of statistical machine learning methods. These methods, however, are data-intensive dependent on assumptions about data distribution. This research offers an integrated fuzzy model address such issues. study proposes reduce problems. The ...

Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non-crisp (symbolic interval and fuzzy) numbers. ...

Herding is the process of bringing individuals (e.g. animals) together into a group. More specifically, we consider self– organized herding as the process of moving a set of individuals to a given number of locations (cluster centers) without any external control. We formally describe the relation between herding and clustering and show that any clustering model can be used to control herding p...