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 ...

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...

Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in ...

Relational clustering is an extension of clustering for relational data. Fuzzy c-Medoids (FCMdd) based linear fuzzy clustering extracts intrinsic local linear substructures from relational data. However this linear clustering was affected by noise or outliers because of using Euclidean distance. Alternative Fuzzy c-Means (AFCM) is an extension of Fuzzy c-means, in which a modified distance meas...

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...

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...

Probabilistic clustering techniques use the concept of memberships to describe the degree by which a vector belongs to a cluster. The use of memberships provides probabilistic methods with more realistic clustering than “hard” techniques. However, fuzzy schemes (like the Fuzzy c Means algorithm, FCW are open sensitive to outliers. We review four existing algorithms, devised to reduce this sensi...

Fuzzy C-means is a widely used clustering algorithm in data mining. Since traditional fuzzy C-means algorithms do not take spatial information into consideration, they often can’t effectively explore geographical data information. So in this paper, we design a Spatial Distance Weighted Fuzzy C-Means algorithm, named as SDWFCM, to deal with this problem. This algorithm can fully use spatial feat...