This chapter tries to answer the fundamental question of what main contributions of fuzzy clustering to the theory of cluster analysis from theoretical viewpoints. While fuzzy clustering is thought to be clearly useful by users of this technique, others think that the concept of fuzziness is not needed in clustering. Thus the usefulness of fuzzy clustering is not trivial. The discussion here is...

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike tr...

The recent advances of array technologies have made it possible to monitor huge amount of genes expression data. Clustering, for example, hierarchical clustering, self-organizing maps (SOM), kmeans clustering, has become important analysis for such gene expression data. We have applied the Fuzzy adaptive resonance theory (Fuzzy ART) [5] to the gene clustering of DNA microarray data and the clus...

Brain tumor is the most deadly disease that affects human life span. To segment the brain tumor part, many segmentation techniques have been emerged in image processing like region based Segmentation, Boundary based segmentation. In this paper, several entropies based methods and several cluster techniques are compared and analyzed for brain tumor segmentation. Several entropies such as rough e...

DNA algorithm and fuzzy evolutionary clustering techniques are used to classify damaged images and to reconstruct the original images. Experimental results show both methods are far more effective than the use of genetic algorithms or c-means clustering. Particularly, the method of fuzzy evolutionary clustering provides very fast convergence and accurate image reconstruction with absolute certa...

Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to ...

In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double C...

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set...

In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...