The weighting exponentm is called the fuzzifier that can influence the performance of fuzzy c-means (FCM). It is generally suggested that mA[1.5,2.5]. On the basis of a robust analysis of FCM, a new guideline for selecting the parameter m is proposed. We will show that a large m value will make FCM more robust to noise and outliers. However, considerably large m values that are greater than the...

Clustering on target positions is a class of centralized algorithms used to calculate the surveillance robots' displacements in Cooperative Target Observation (CTO) problem. This work proposes and evaluates Fuzzy C-means (FCM) Density-Based Spatial Applications with Noise (DBSCAN) K-means (DBSk) based self-tuning clustering for CTO problem compares its performances that K-means. Two random moti...

Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FCM) or similar clustering mechanisms. Several improvements have been made to the standard FCM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents ...

Fuzzy C-means clustering (FCM) is an important technique used in cluster analysis. The standard FCM algorithm calls the centroids to be randomly initialized resulting in the requirement of making estimations from expert users to determine the number of clusters. To overcome these observed limitations of applying the FCM algorithm, an efficient image segmentation model, Hybrid Fuzzy C-means Algo...

Gath–Geva (GG) algorithm is one of the most popular methodologies for fuzzy c-means (FCM)-type clustering of data comprising numeric attributes; it is based on the assumption of data deriving from clusters of Gaussian form, a much more flexible construction compared to the spherical clusters assumption of the original FCM. In this paper, we introduce an extension of the GG algorithm to allow fo...

−Segmentation is a difficult and challenging problem in the magnetic resonance images, and it considered as important in computer vision and artificial intelligence. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. In this paper, we present a novel FCM algorithm for weighted bias (also called intensity in-...

Abstract The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM ensemble approach that takes local information into account (LI_BIFCM) overcome these challenges increase quality. First, various membership matrices are created after running multiple times, based on the randomization i...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...

An efficient noise reduction approach is proposed by combining Robust Outlyingness Ratio (ROR) which measures how impulse like each pixel is, with noise adaptive fuzzy switching median filter (NAFSM) and fuzzy c-means (FCM) segmentation. Based on the ROR values all the pixels are divided into four levels. Then in the coarse and fine stage introduce the NAFSM filter that optimizes the performanc...