Fast fuzzy clustering

Modification of the Fast Global K-means Using a Fuzzy Relation with Application in Microarray Data Analysis

Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively ...

A Fast Fuzzy Clustering Algorithm

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of dista...

Dna Algorithm and Fuzzy Evolutionary Clustering for Image Reconstruction

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...

Fast Color Quantization via Fuzzy Clustering

Neural Network Based Fuzzy C-MEANS Clustering Algorithm

In this paper, fuzzy c-means algorithm uses neural network algorithm is presented. In pattern recognition, fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms to group the high dimensional data into clusters. The proposed work involves two steps. First, a recently developed and Enhanced Kmeans Fast Leaning Artificial Neural Network (KFLANN) frame work is use...

Fuzzy c-Means Herding

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...