Search results for "Fuzzy"
showing 10 items of 747 documents
Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery
2013
Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight cluster…
Fuzzy modeling of solar irradiance on inclined surfaces
2003
A model of solar irradiance on arbitrarily oriented inclined surfaces is proposed, based on fuzzy logic procedures. The behavior of the proposed model is similar to that of other models of increased performance such as the models of Perez or Gueymard, though it requires only a very limited number of classes and adjustable parameters. The use of fuzzy clustering optimizes the number and definition of the sky categories. The model considers overlapping clusters and allows an improved description of the sky situations close to the transition zone between contiguous categories.
Gravitational weighted fuzzy c-means with application on multispectral image segmentation
2014
This paper presents a novel clustering approach based on the classic Fuzzy c-means algorithm. The approach is inspired from the concept of interaction between objects in physics. Each data point is regarded as a particle. A specific weight is associated with each data particle depending on its interaction with other particles. This interaction is induced by attraction forces between pairs of particles and the escape velocity from other particles. Classification experiments using two data sets from UCI repository demonstrate the outperformance of the proposed approach over other clustering algorithms. In addition, results demonstrate the effectiveness of the proposed scheme for segmentation …
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
2022
Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…
Accurate detection and characterization of corner points using circular statistics and fuzzy clustering
1998
Accurate detection and characterization of corner points in grey level images is considered as a pattern recognition problem. The method considers circular statistic tests to detect 2D features. A fuzzy clustering algorithm is applied to the edge orientations near the prospective corners to detect and classify them. The method is based on formulating hypotheses about the distribution of these orientations around an edge, corner or other 2-D feature. The method may provide accurate estimates of the direction of the edges that converge in a corner, along with their confidence intervals. Experimental results show the method to be robust enough against noise and contrast changes. Fuzzy membersh…
A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm
2015
The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-dow…
Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic
2016
Distributed denial-of-service (DDoS) attacks are one of the most serious threats to today’s high-speed networks. These attacks can quickly incapacitate a targeted business, costing victims millions of dollars in lost revenue and productivity. In this paper, we present a novel method which allows us to timely detect application-layer DDoS attacks that utilize encrypted protocols by applying an anomaly-based approach to statistics extracted from network packets. The method involves construction of a model of normal user behavior with the help of weighted fuzzy clustering. The construction algorithm is self-adaptive and allows one to update the model every time when a new portion of network tr…
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
2017
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…
Fuzzy Discrete Event Simulation for Fuzzy Production Systems Analysis
1998
Abstract Fuzzy production systems are characterised by vagueness in data and requirements that very often cannot be reduced to stochastic models. Therefore, such production systems cannot be analysed by using classical techniques such as Queue Theory or Discrete Simulation Analysis. On the other hand the great diffusion of Fuzzy Production environments in small and medium enterprises claims for the development of new analysis tools. This paper proposes a new approach to Discrete Event Simulation able to treat with fuzzy variables. A new methodology has been proposed to process fuzzy information within discrete event simulation and a prototype of a Fuzzy Discrete Event Simulator has been dev…
Decision Suport System for Manufacturing Processes Reengineering based upon Fuzzy Logic Techniques
2012
Abstract This work presents a method for taking the decision of reengineering a production system, based upon fuzzy techniques. The main advantage of this method is, after authors' opinion, is the ease of its implementation together with the reduced time for gathering data and processing it. Multi-variable decision systems are usually based upon complicated mathematical methods and involved a large amount of data to be processed. The fuzzy approach presented here is based only on five input variables and one output variable. The data for the model are gathered by simple queries and quizzes. Human perception, the main point of fuzzy logic, is widely used here for gathering input data for the…