With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters b...
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ISBN:
(纸本)9781424421138
With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters by fuzzy c-means algorithm (FcM). Then the result is tested by Support Vector Machine (SVM) associated with a fuzzy membership function to confirm whether the data set could be classified. Finally, the process is repeated and the clustering result can be obtained. With this unsupervised algorithm, not only does the training data set need no labeling, but also the cluster number needs no specifying. Experiments over networks connection records from KDD cUP 1999 data set were implemented to evaluate the proposed method. To obtain an appropriate training data set and overcome the low efficiency in processing the high dimensional data seta a cross method and a feature selection algorithm based on mutual information were applied respectively in experiments. The result dearly shows the outstanding performance of the proposed method in decision of cluster number and effect of intrusion detection.
Medical image segmentation plays an important role in medical image analysis and visualization. The fuzzyc-means (FcM) is one of the well-known methods in the practical applications of medical image segmentation. FcM...
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ISBN:
(纸本)9783540896388
Medical image segmentation plays an important role in medical image analysis and visualization. The fuzzyc-means (FcM) is one of the well-known methods in the practical applications of medical image segmentation. FcM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FcM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FcM method and provides reliable and efficient processing on cT and MRI image segmentation.
Intensity inhomogeneity or intensity non-uniformity (INU) is ail undesired phenomenon that represents the main obstacle for MR, image segmentation and registration methods. Various techniques have been proposed to eli...
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ISBN:
(纸本)9783540875581
Intensity inhomogeneity or intensity non-uniformity (INU) is ail undesired phenomenon that represents the main obstacle for MR, image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This paper proposes a multiple stage fuzzyc-means (FcM) based algorithm for the estimation and compensation of IN U, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, based oil a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.
With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters b...
详细信息
With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-cMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters by fuzzy c-means algorithm (FcM). Then the result is tested by Support Vector Machine (SVM) associated with a fuzzy membership function to confirm whether the data set could be classified. Finally, the process is repeated and the clustering result can be obtained. With this unsupervised algorithm, not only does the training data set need no labeling, but also the cluster number needs no specifying. Experiments over networks connection records from KDD cUP 1999 data set were implemented to evaluate the proposed method. To obtain an appropriate training data set and overcome the low efficiency in processing the high dimensional data set, a cross method and a feature selection algorithm based on mutual information were applied respectively in experiments. The result clearly shows the outstanding performance of the proposed method in decision of cluster number and effect of intrusion detection.
cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. Especially, fuzzyclustering has been widely studied and ...
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cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. Especially, fuzzyclustering has been widely studied and applied in a variety of key areas and fuzzycluster validation plays a very important role in fuzzyclustering. This paper introduces the fundamental concepts of cluster validity, and presents a review of fuzzycluster validity indices available in the literature. We conducted extensive comparisons of the mentioned indices in conjunction with the fuzzyc-meansclustering algorithm on a number of widely used data sets, and make a simple analysis of the experimental results. (c) 2007 Elsevier B.V All rights reserved.
Automated brain MR image segmentation is a challenging problem and received significant attention lately. Various techniques have been proposed, several improvements have been made to the standard fuzzyc-means (FcM) ...
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ISBN:
(纸本)9783540730392
Automated brain MR image segmentation is a challenging problem and received significant attention lately. Various techniques have been proposed, several improvements have been made to the standard fuzzyc-means (FcM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FcM algorithm, which aims at accurate segmentation in case of mixed noises, and performs at a high processing speed. As a first step, a scalar feature value is extracted from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards using the histogram-based approach of the enhanced FcM algorithm. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FcM-based techniques. The produced segmentation and fuzzy membership values can serve as excellent support for level set based cortical surface reconstruction techniques.
Based on the uncertainty and fuzziness of remote sensing images, a dot density function weighted fuzzyc-means (WFcM) clustering algorithm is proposed to carry out the fuzzyclassification or the hard classification o...
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ISBN:
(纸本)9781424412112
Based on the uncertainty and fuzziness of remote sensing images, a dot density function weighted fuzzyc-means (WFcM) clustering algorithm is proposed to carry out the fuzzyclassification or the hard classification of remote sensing images. First, the algorithmconsidering data spatial distributing information and classification fuzziness is described. fuzzy c-means algorithm is an unsupervised fuzzyclassification method. clustering precision of the algorithm is affected by its equal partition trend for data sets, which leads that the optimal solution of the algorithm may not be the correct partition in the data set of which cluster sample numbers are difference greatly. In order to overcome this drawback, a dot density function WFcM algorithm is proposed in this paper. The method has not only overcome the limitation of FcM to certain extent, but also been favorable convergence. Then the WFcM algorithm would be compared with the K-meansalgorithms by experiments in LANDSAT TM image. Finally classification result of the algorithms is analyzed systematically, and the experiment result shows the WFcM algorithmcan improve classification accuracy for remote sensing images.
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks....
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ISBN:
(纸本)9781424408122
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks....
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In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.
This work describes a novel technique for automating the post-fabrication circuit tuning process. A training set that characterizes the behaviour of the circuit under test is first constructed. The data in this set co...
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This work describes a novel technique for automating the post-fabrication circuit tuning process. A training set that characterizes the behaviour of the circuit under test is first constructed. The data in this set consists of input measurement vectors with no output attributes, and is clustered via unsupervised learning algorithm in order to explore its underlying structure and correlations. The generated clusters are labeled and utilized in circuit tuning by calculating the value(s) of the tuning parameter(s). Three prominent and fundamentally different unsupervised learning algorithms, namely, the self-organizing map, the Gaussian mixture model, and the fuzzy c-means algorithm are employed and their performance is compared. The experimental results demonstrate that the proposed technique provides a robust and efficient circuit tuning approach.
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