Search results for "Clustering"
showing 10 items of 446 documents
Minimum message length clustering: an explication and some applications to vegetation data
2001
In this paper, we examine the application of a particular approach to induction, the minimum message length principle and illustrate some of the problems that can be addressed through its use. The MML principle seeks to identify an optimal model within some specified parameterised class of models and for this paper we have chosen to concentrate on a single model class, that of mixture separation or fuzzy clustering. The first section presents, in outline, an MML methodology for fuzzy clustering. We then present some applications, including the nature of the within-cluster model, examination of the univocality of results for different groups of species and the effectiveness of presence data …
Fuzzy C-Means Inspired Free Form Deformation Technique for Registration
2009
This paper presents a novel method aimed to free form deformation function approximation for purpose of image registration. The method is currently feature-based. The algorithm is inspired to concepts derived from Fuzzy C-means clustering technique such as membership degree and cluster centroids. After algorithm explanation, tests and relative results obtained are presented and discussed. Finally, considerations on future improvements are elucidated.
Scalable Clustering by Iterative Partitioning and Point Attractor Representation
2016
Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon the concept of synchronization. Inherited from the powerful concept of synchronization, the proposed algorithm, CIPA (Clustering by Iterative Partitioning and Point Attractor Representations), is capable of handling very large datasets by iteratively partitioning them into thousands of subsets and clustering each subset separately. Using dynamic clustering by synchronization, each subset is then represented by a set of point attractors and outliers. Finally, CIPA identifies the…
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…