Graph clustering is a very popular research field with numerous practical applications. Here we focus on finding fuzzy clusters of nodes in unweighted, undirected, and irreflexive graphs. We introduce three new algorithms for fuzzy graph clustering (Newman–Girvan NERFCM, Small World NERFCM, Signal NERFCM). Each of these three new algorithms uses a popular algorithm for crisp graph clustering an...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. Fo...

Bijayalaxmi Panda, Soumya Sahoo, Sovan Kumar Patnaik Abstract— Cluster analysis is one of the major techniques in pattern recognition, which is basically considered as one of the unsupervised learning technique. We can apply clustering techniques in various areas like clustering medicine, business, engineering systems and image processing, etc.,The traditional hard clustering methods restrict t...

This paper considers dissimilarity measures and clustering techniques for two special cases of set-defined objects: fuzzy granules and subsequence time series. To deal with clustering of such kind of objects, we propose two implementations that generalize the Fuzzy C-Means algorithm to granular feature spaces. Granular computing is a paradigm oriented towards capturing and processing meaningful...

Two-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k -means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode cl...

Web usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. Recent attempts have adapted the C-means clustering algorithm as well as genetic algorithms to find sets of clusters .In this paper; we have proposed a new framework to improve the web sessions’ cluster quality from fuzzy...

In this paper, we report results from comparative study on various related and relevant aspects of the digital watermarking such as image authentication techniques using fragile watermarking, fuzzy clustering and genetically inspired watermarking techniques for integrity verification. This is carried out with intent to develop an understanding of their working, contained challenges, possible at...

Lung lesion segmentation refers to the process of partitioning an image into mutually exclusive regions. This study gives a new approach to K-means clustering technique (K-CT) integrated with Fuzzy C-means algorithm for lung segmentation. In the study, large number of images with various types of segmentation was selected and examined. It is followed by thresholding and level set segmentation s...