Fuzzy c-means (FCM) clustering is based on minimizing the fuzzy within cluster scatter matrix trace but FCM neglects the between cluster scatter matrix trace that controls the distances between the class centroids. Based on the principle of cluster centers separation, fuzzy cluster centers separation (FCCS) clustering is an extended fuzzy c-means (FCM) clustering algorithm. FCCS attaches import...

Geometry conforming meshing techniques such as the finite element method (FEM) face a big challenge when dealing with complex and heterogeneous microstructures. Therefore, efficient simulation methods are needed combining accurate morphological reproducibility computational efficiency. For problems, cell (FCM) is promising approach, which uses Cartesian grid – independent of geometry leading to...

This paper presents a comparison between fuzzy cognitive maps (FCM) and the fuzzy analytic network process (FANP) as two well-known techniques to model causality during the process of assessing readiness of an organisation to implement a change. FCM is well known generally, but has been rarely used in assessing readiness for change. In this paper, we demonstrate several advantages of FCM for ch...

The most widely used clustering algorithm implementing the fuzzy philosophy is Fuzzy CMeans (FCM) .In this paper, we have proposed a new Hybrid FCM with Genetic Algorithm (GA), we get an improved FCM algorithm which has not only the global search capability of GA but also the local search capability of FCM, and hence can better solve the clustering problem. An improved version of this hybrid cl...

Patterns in stress hormone (glucocorticoid: GC) levels and their relationship to reproductive condition in natural populations are rarely investigated. In this study, we (1) validate an enzyme-immunoassay to measure fecal cortisol metabolite (FCM) levels in North American red squirrels (Tamiasciurus hudsonicus), and (2) examine relationships between FCM levels and reproductive condition in a fr...

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (...

The effects of feeding Fusarium moniliforme culture material, containing known concentrations of fumonisin Bj (FBi), were studied in turkey poults. Day-old poults were allotted randomly to dietary treatments containing 0, 0.41, 0.82, 1.23, 1.64, 2.87, 4.10, 5.33, 6.56, and 7.79% fumonisin culture material (FCM). These levels of FCM supplied 0, 25, 50, 75, 100, 175, 250, 325, 400, and 475 mg FBi...

Application of flow cytometry (FCM) to microbial analysis of milk is hampered by the presence of milk proteins and lipid particles. Here we report on the development of a rapid (/= 0.98) between the FCM assay and the more conventi...

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artifici...

Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ...