In the process of image segmentation, the classic Fuzzy C-Means (FCM) algorithm is time-consuming and depends heavily on initialization center. Based on Graphic Processing Unit (GPU), this paper proposes a novel FCM algorithm by improving the computational formulas of membership degree and the update criterion of cluster centers. Our algorithm can initialize cluster centers purposefully and fur...

Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non-crisp (symbolic interval and fuzzy) numbers. ...

We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-population...

The purpose of multicriteria clustering is to locate groups alternatives that have comparable qualities and been examined across multiple criteria. An ordered profile a well-known problem, the fuzzy c-means (FCM) technique one most broadly used in every field life. At present, FCM for partitioning information into numerous clusters which are still lacking priority relations. To address problem ...

The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzz...

As special aggregation functions, overlap functions have been widely used in the soft computing field. In this work, with aid of two new groups fuzzy mathematical morphology (FMM) operators were proposed and applied to image processing, they obtained better results than existing algorithms. First, based on structuring elements, first group FMM (called OSFMM operators) was proposed, their proper...

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome th...

In this paper we present an hybrid approach which integrate Fuzzy C-Means (FCM) algorithms and Genetic Algorithms (GAs) to design an optimal classifier for the specific classification problem. This integration allows automatic generation of an classifier system, with an optimized subset of features, from a database of examples. The generated classifier strongly outperform the classic FCM algori...

Protein sequence motifs are very important to the analysis of biologically significant conserved regions to determine the conformation, function and activities of the proteins. These sequence motifs are identified from protein sequence segments generated from large number of protein sequences. All generated sequence segments may not yield potential motif patterns. In this paper, short recurring...

In this paper an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM) technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the m...