Recently, Fuzzy Grey Cognitive Map (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it is focused on solving problems with high uncertainty, under discrete incomplete and small data sets. The FGCM nodes are variables, representing grey concepts. The relationships between nodes are represented by directed edges. An edge linking two nodes models the grey causal...

Modeling of large systems requires high level of expertise to properly identify and represent complex interrelationships between various elements. In addition to creation of a network of system’s elements and their relationships, very important contribution to understanding of its behavior we can get from human experts. Naturally, humans express their experience and beliefs descriptively, empha...

Fuzzy Cognitive Maps (FCMs) have found many applications in social -financial -political problems. In this paper we propose a method of FCM operation, which can be used to represent and control any real system, including traditional electro-mechanical systems. In the proposed approach the FCM reaches its equilibrium point using direct feedback from the node values of the real system and the lim...

The optimization of a Fuzzy Cognitive Map model for the supervision and monitoring of the radiotherapy process is proposed. This is performed through the minimization of the corresponding objective function by using the Particle Swarm Optimization and the Differential Evolution algorithms. The proposed approach determines the cause–effect relationships among the concepts of the supervisor–Fuzzy...

The goal of this paper is to further investigate the extreme behaviour of the proportional membership model (FCPM) in contrast to the central tendency of fuzzy c-means (FCM). A data set from the ̄eld of psychiatry has been used for the experimental study, where the cluster prototypes are indeed extreme, expressing the concept of `ideal type'. While augmenting the original data set with patients...

In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the in1uence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means (AFCM) clustering algorithms. These al...

Relational fuzzy c-means (RFCM) is an algorithm for clustering objects represented in a pairwise dissimilarity values in a dissimilarity data matrix D. RFCM is dual to the fuzzy c-means (FCM) object data algorithm when D is a Euclidean matrix. When D is not Euclidean, RFCM can fail to execute if it encounters negative relational distances. To overcome this problem we can Euclideanize the relati...

This paper proposes a new fuzzy approach for automatic segmentation of normal and pathological brain MRI volumetric data sets. MRI is generally useful for brain tumor deduction because it provide more detailed information about its type, position, size. Brain tumor segmentation is the separation of different tumor tissues from normal brain tissue. In automatic brain segmentation MRI is a sophis...

The fact of building an accurate classification and prediction system remains one of the most significant challenges in knowledge discovery and data mining. In this paper, a Knowledge Discovery (KD) framework is proposed; based on the integrated fuzzy approach, more specifically Fuzzy C-Means (FCM) and the new Multiple Support Classification Association Rules (MSCAR) algorithm. MSCAR is conside...

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...