This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters’ centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for a given dataset using the PBM index. Topics of Interest: Unsupervised Classification, Fuzzy c-Means, ...

This contribution describes using fuzzy c-means clustering method in image segmentation. Segmentation method is based on a basic region growing method and uses membership grades’ of pixels to classify pixels into appropriate segments. Images were in RGB color space, as feature space was used L*u*v* color space. Results were obtained on five color test images by experimental simulations in Matlab.

Clustering is a task of assigning a set of objects into groups called clusters. In general the clustering algorithms can be classified into two categories. One is hard clustering; another one is soft (fuzzy) clustering. Hard clustering, the data’s are divided into distinct clusters, where each data element belongs to exactly one cluster. In soft clustering, data elements belong to more than one...

A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the /rst phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...