This paper presents a latest survey of different technologies using fuzzy clustering algorithms. Clustering approach is widely used in biomedical field like image segmentation. A different methods are used for medical image segmentation like Improved Fuzzy C Means(IFCM), Possibilistic C Means(PCM),Fuzzy Possibilistic C Means(FPCM), Modified Fuzzy Possibilistic C Means(MFPCM) and Possibilistic F...

• J. Beringer and E. Hüllermeier. Efficient instance based learning on data streams. Adaptive optimization of the number of clusters in fuzzy clustering. Fuzzy clustering of parallel data streams. Adaptive optimization of the number of clusters in fuzzy clustering.

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, t...

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 ...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The consolidation of neural networks and fuzzy logic in neurofuzzy models provides learning as well as readability. This paper aims at modeling the input-output relationship with fuzzy IF-THEN rules by using fuzzy clustering technique. The main difference between fuzzy cluster...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The consolidation of neural networks and fuzzy logic in neurofuzzy models provides learning as well as readability. This paper aims at modeling the input-output relationship with fuzzy IF-THEN rules by using fuzzy clustering technique. The main difference between fuzzy cluster...

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approache...

Using Greedy Clustering Method to Solve Capacitated Location-Routing Problem with Fuzzy Demands Abstract In this paper, the capacitated location routing problem with fuzzy demands (CLRP_FD) is considered. In CLRP_FD, facility location problem (FLP) and vehicle routing problem (VRP) are observed simultaneously. Indeed the vehicles and the depots have a predefined capacity to serve the customerst...

The purpose of this paper is to improve the performance of the kernel fuzzy clustering model by introducing a self-organized algorithm. A conventional kernel fuzzy clustering model is defined as a model which is an improved additive fuzzy clustering. The purpose of this conventional model is to obtain a clearer result by consideration of the interaction of clusters. This paper proposes a fuzzy ...

In this paper, we propose a Context-based Gustafson-Kessel (CGK) clustering that builds Information Granulation (IG) in the form of fuzzy set. The fundamental idea of this clustering is based on Conditional Fuzzy C-Means (CFCM) clustering introduced by Pedrycz. The proposed clustering develops clusters preserving homogeneity of the clustered patterns associated with the input and output space. ...