Results 51 to 60 of about 867,994 (193)
Promet (Zagreb), 2012 This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on ...
Iman Aghayan +2 more
doaj +1 more source
A Heuristic Approach to Possibilistic Clustering for Fuzzy Data
Journal of Information and Organizational Sciences, 2008 The paper deals with the problem of the fuzzy data clustering. In other words, objects attributes can be represented by fuzzy numbers or fuzzy intervals.
Dmitri A. Viattchenin
doaj
Improvement of the ANFIS-based wave predictor models by the Particle Swarm Optimization
Journal of Ocean Engineering and Science, 2020 In this paper, the Particle Swarm Optimization (PSO) algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System (ANFIS) model drawbacks in prediction of wind –driven waves.
Morteza Zanganeh
doaj +1 more source
Fuzzy Subspace Clustering [PDF]
, 2009 In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset.
openaire +2 more sources
International Journal of Geophysics, 2015 Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive.
A. Stanley Raj +2 more
doaj +1 more source
Tools for analysing fuzzy clusters of sequences data [PDF]
Demographic ResearchBACKGROUND: Sequence analysis is a set of tools increasingly used in demography and other social sciences to analyse longitudinal categorical data. Typically, single (e.g., education trajectories) or multiple parallel temporal processes (e.g., work and ...
Raffaella Piccarreta +1 more
doaj +1 more source
Color Image Segmentation Using Fuzzy C-Regression Model
Advances in Fuzzy Systems, 2017 Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the
Min Chen, Simone A. Ludwig
doaj +1 more source
A Fuzzy Granular K-Means Clustering Method Driven by Gaussian Membership Functions
MathematicsThe K-means clustering algorithm is widely applied in various clustering tasks due to its high computational efficiency and simple implementation.
Junjie Huang +4 more
doaj +1 more source
Robust Self-Sparse Fuzzy Clustering for Image Segmentation
IEEE Access, 2020 Traditional fuzzy clustering algorithms suffer from two problems in image segmentations. One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy memberships.
Xiaohong Jia +5 more
doaj +1 more source
Fuzzy clustering of intuitionistic fuzzy data
International Journal of Business Intelligence and Data Mining, 2008 Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More specifically, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which ...
Pelekis, N. +3 more
openaire +2 more sources