With the increase of data volume and visitor volume, the website faces great challenge in the environment of network. How to know the users’ requirements rapidly and effectively and recommend the required information to the user becomes the research direction of all websites. The researchers of recommendation system propose a series of recommendation system models and algorithms for the user. T...

Health impact assessment (HIA) is often used to determine ex ante the health impact of an environmental policy or an environmental intervention. Underpinning any HIA is the framing assumption, which defines the causal pathways mapping environmental exposures to health outcomes. The sensitivity of the HIA to the framing assumptions is often ignored. A novel method based on fuzzy cognitive map (F...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

During radiotherapy many decisions have to be made and the development of an advanced decision support system that take into consideration different and discipline factors in determining the dose calculation for radiotherapy treatment would be very useful. The implementation of Fuzzy Cognitive Maps (FCM) for decisionmaking issues in radiotherapy is proposed. FCMs can handle with imprecise, unce...

Fuzzy Cognitive Maps (FCMs) have been proposed to represent causal reasoning by using numeric processing. They graphically represent uncertain causal reasoning. In the resonant states, there emerges a limit cycle or a hidden pattern, which is a FCM inference. However, there are some shortcomings concerned with knowledge representation in the conventional FCMs. In this paper, we propose Extended...

A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modeled by fuzzy theory. The FCM algorithm has become the most widely used method in fuzzy clustering. Although, there are some successful applications of FCM have been proposed, a disad...

A hybrid method based on the structural equation model (SEM) and a fuzzy cognitive map (FCM) was developed to study influences of construction safety risks (CSR) metro tunnels constructed by mining project risk (PR). An SEM used identify verify correlations between CSR PR. FCM adopting verified standardized causality path coefficient in SEM. On this basis, predictive reasoning diagnostic were p...

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...

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM...

An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in s...