Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the qualit...
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Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the quality of an algorithmically generated partition and accordingly guide an amelioration process through split-and-merge operations. (c) 2000 Published by Elsevier Science B.V. All rights reserved.
It is well known that fuzzyc-means(FcM) algorithm is one of the most popular methods of cluster ***,the traditional FcM algorithm does not work for the interval-valued data and fuzzy-valued *** this end,a feature map...
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It is well known that fuzzyc-means(FcM) algorithm is one of the most popular methods of cluster ***,the traditional FcM algorithm does not work for the interval-valued data and fuzzy-valued *** this end,a feature mapping method is proposed to preprocess these special type data,and then the traditional FcM algorithmcan also be employed to analyze the interval-valued and fuzzy-valued ***,a novel FcM clustering algorithm is formed for interval-valued data and fuzzy-valued *** experimental result demonstrates its effectiveness.
In this paper, a novel variable-rate vector quantizer (VQ) design algorithm using fuzzyclustering technique is presented. The algorithm, termed fuzzy entropy-constrained VQ (FEcVQ) design algorithm, has a better rate...
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In this paper, a novel variable-rate vector quantizer (VQ) design algorithm using fuzzyclustering technique is presented. The algorithm, termed fuzzy entropy-constrained VQ (FEcVQ) design algorithm, has a better rate-distortion performance than that of the usual entropy-constrained VQ (EcVQ) algorithm for variable-rate VQ design. When performing the fuzzyclustering, the FEcVQ algorithmconsiders both the usual squared-distance measure, and the length of channel index associated with each codeword so that the average rate of the VQ can be controlled. In addition, the membership function for achieving the optimal clustering for the design of FEcVQ are derived. Simulation results demonstrate that the FEcVQ can be an effective alternative for the design of variable-rate VQs.
In multivariate statistical methods, it is important to identify influential observations for a reasonable interpretation of the data structure, In this paper, we propose a method for identifying influential data in t...
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In multivariate statistical methods, it is important to identify influential observations for a reasonable interpretation of the data structure, In this paper, we propose a method for identifying influential data in the fuzzyc-means (FcM) algorithm, To investigate such data, we consider a perturbation of the data points and evaluate the effect of a perturbation. As a perturbation, we consider two cases: one is the case in which the direction of a perturbation is specified and the other is the case in which the direction of a perturbation is not specified, By computing the change in the clustering result of FcM when given data points are slightly perturbed, we can look for data points that greatly affect the result, Also, we confirm an efficacy of the proposed method by numerical examples.
A codeword-rotation algorithm is proposed for vector quantization (VQ) of images. A novel binary classifier is presented to preclassify the training vectors into six classes including edge blocks and nonedge blocks. T...
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A codeword-rotation algorithm is proposed for vector quantization (VQ) of images. A novel binary classifier is presented to preclassify the training vectors into six classes including edge blocks and nonedge blocks. The VQ codebook is generated by applying the modified fuzzyc-means (MFcM) algorithm to the training vectors of each class. Similar edge blocks are rotated and coalesced during the edge subcodebook generation process. Furthermore, two schemes for designing the encoder and decoder are also presented. compared with the basic VQ system constructed by the LEG algorithm, the new method results in a considerable reduction in codebook size and computation time of codebook generation. More importantly, the visual quality achieved is better than the basic VQ system.
The fuzzyclustering Problem (FcP) is a mathematical program which is difficult to solve since it is nonconvex, which implies possession of many local minima. The fuzzyc-means heuristic is the widely known approach t...
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The fuzzyclustering Problem (FcP) is a mathematical program which is difficult to solve since it is nonconvex, which implies possession of many local minima. The fuzzyc-means heuristic is the widely known approach to this problem, but it is guaranteed only to yield local minima. In this paper, we propose a new approach to this problem which is based on tabu search technique, and aims at finding a global solution of FcP. We compare the performance of the algorithm with the fuzzy c-means algorithm. (c) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.
Analysis of magnetic resonance images (MRI) of the brain permits the identification and measurement of brain compartments. These compartments include normal subdivisions of brain tissue, such as gray matter, white mat...
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ISBN:
(纸本)0819417823
Analysis of magnetic resonance images (MRI) of the brain permits the identification and measurement of brain compartments. These compartments include normal subdivisions of brain tissue, such as gray matter, white matter and specific structures, and also include pathologic lesions associated with stroke or viral infection. A fuzzy system has been developed to analyze images of animal and human brain, segmenting the images into physiologically meaningful regions for display and measurement. This image segmentation system consists of two stages which include a fuzzy rule-based system and fuzzy c-means algorithm (FcM). The first stage of this system is a fuzzy rule-based system which classifies most pixels in MR images into several known classes and one `unclassified' group, which fails to fit the predetermined rules. In the second stage, this system uses the result of the first stage as initial estimates for the properties of the compartments and applies FcM to classify all the previously unclassified pixels. The initial prototypes are estimated by using the averages of the previously classified pixels. The combined processes constitute a fast, accurate and robust image segmentation system. This method can be applied to many clinical image segmentation problems. While the rule-based portion of the system allows specialized knowledge about the images to be incorporated, the FcM allows the resolution of ambiguities that result from noise and artifacts in the image data. The volumes and locations of the compartments can easily be measured and reported quantitatively once they are identified. It is easy to adapt this approach to new imaging problems, by introducing a new set of fuzzy rules and adjusting the number of expected compartments. However, for the purpose of building a practical fully automatic system, a rule learning mechanism may be necessary to improve the efficiency of modification of the fuzzy rules.
The `fuzzyclustering' problem is investigated. Interesting properties of the points generated in the course of applying the fuzzy c-means algorithm are revealed using the concept of reduced objective function. We...
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The `fuzzyclustering' problem is investigated. Interesting properties of the points generated in the course of applying the fuzzy c-means algorithm are revealed using the concept of reduced objective function. We investigate seven quantities that could be used for stopping the algorithm and prove relationships among them. Finally, we empirically show that these quantities converge linearly.
We propose a design scheme for a hierarchical fuzzy pattern matching classifier (HFPMc) and apply it to the tire tread pattern recognition problem. In this design scheme, a binary decision tree is constructed at first...
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We propose a design scheme for a hierarchical fuzzy pattern matching classifier (HFPMc) and apply it to the tire tread pattern recognition problem. In this design scheme, a binary decision tree is constructed at first by using fuzzyc-means (FcM) algorithm. At each node, a representative subset of features which can split best the labelled data into two dissimilar groups is selected from all the available features on the base of cluster validity. The cluster validity is evaluated under the two criteria. The one is the polarization degree, and the other is whether all the samples of a class belong to the same cluster or not. Then, a hierarchical cluster structure for the HFPMc is reconstructed by combining the successive nodes formed by the same representative subset of features. As the hierarchical classifier, is used a fuzzy pattern matching classifier in which the designer's intuitive knowledges about the pattern recognition problem can be easily incorporated. At each subhierarchy, the reference fuzzy sets and prototypes for the HFPMc are defined based on the cluster centers of the corresponding subhierarchy. The proposed design scheme is applied to the design of a HFPMc for the tire tread pattern recognition. The design procedure including feature extraction is described in detail. Experimental results show the usefulness of the proposed design scheme.
In this paper, a fuzzycompetitive learning (FcL) paradigm adopting a principle of learn according to how well it wins is proposed, based upon which three existing competitive learning algorithms, namely, the unsuperv...
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In this paper, a fuzzycompetitive learning (FcL) paradigm adopting a principle of learn according to how well it wins is proposed, based upon which three existing competitive learning algorithms, namely, the unsupervised competitive learning, learning vector quantization, and frequency sensitive competitive learning, are fuzzified to form a class of FcL algorithms. Unlike the crisp competitive learning algorithms where only one neuron will win and learn at each competition, every neuron in the proposed FcL networks to a certain degree wins, depending on its distance to the input pattern, and learns the pattern accordingly. Thus, the concept of win has been formulated as a fuzzy set and the network outputs become the win memberships (in [0, 1]) of the competing neurons. compared with the crisp competitive learning algorithms, the proposed fuzzyalgorithms consist of various distinctive features such as i) converging more often to the desired solutions, or equivalently, reducing the likelihood of neuron underutilization that has long been a major shortcoming of crisp competitive learning;ii) better classification rate and generalization performance, especially in overlapping data sets: iii) providing confidence measure of the classification results. These features are demonstrated through numerical simulations of three data sets, including two artificially generated ones and a vowel recognition data set.
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