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检索条件"主题词=fuzzy C-means algorithm"

共 259 条记录，以下是31-40 订阅

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Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models

Analytical and numerical evaluation of the suppressed fuzzy ...

引用

5th International conference on Modeling Decisions for Artificial Intelligence

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Sapientia Univ Transylvania Fac Tech & Human Sci Corunca 547367 Romania Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary

ISBN: (纸本)3540882685

Suppressed fuzzy c-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzy c-means (FcM) algorithm. The authors modified the FcM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper, we clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis. A quasi competitive learning rate (QLR) is introduced first, in order to quantify the effect of suppression. As the investigation of s-FcM's optimality did not provide a precise result, an alternative, optimally suppressed FcM (Os-FcM) algorithm is proposed as a hybridization of FcM and HcM. Both the suppressed and optimally suppressed FcM algorithms underwent the same analytical and numerical evaluations, their properties were analyzed using the QLR. We found the newly introduced Os-FcM algorithm quicker than s-FcM at any nontrivial suppression level. Os-FcM should also be favored because of its guaranteed optimality.

关键词： fuzzy c-means algorithm Suppressed fuzzy c-means algorithm competitive clustering Alternating optimization Learning rate

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A modified fuzzy c-means algorithm for MR brain image segmentation

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4th International conference on Image Analysis and Recognition

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Budapest Hungary Sapienta Hungarian Sci Univ Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540742586

Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FcM) or similar clustering mechanisms. Several improvements have been made to the standard FcM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents a modified FcM-based method that targets accurate and fast segmentation in case of mixed noises. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a context dependent filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards by the histogram-based approach of the enhanced FcM algorithm. Results were evaluated based on synthetic phantoms and real MR images. Test experiments revealed that the proposed method provides better results compared to other reported FcM-based techniques. The achieved segmentation and the obtained fuzzy membership values represent excellent support for deformable contour model based cortical surface reconstruction methods.

关键词： fuzzy c-means algorithm image segmentation noise elimination feature extraction. context dependent filter magnetic resonance imaging

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A Thorough Analysis of the Suppressed fuzzy c-means algorithm

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13th Iberoamerican congress on Progress in Pattern Recognition, Image Analysis and Applications

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540859192

Suppressed fuzzy c-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607-1612 (2003)] with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzy c-means (FcM) algorithm. They added an extra computation step in to the FcM iteration, which created a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membeship grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper we attempt to clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis.

关键词： fuzzy c-means algorithm suppressed fuzzy c-means algorithm competitive clustering alternating optimization

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Improved fuzzy c-means algorithm and Its Application to classification of Remote Sensing Image in chengdu city, china 4

Improved Fuzzy C-Means Algorithm and Its Application to Clas...

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4th IEEE International conference on Progress in Informatics and computing (IEEE PIc)

作者： Han, Dongmei Ji, Jiayu Dai, Yonghui Li, Guowei Fan, Weiguo chen, Huagen Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai Peoples R China Shanghai Financial Informat Technol Key Res Lab Shanghai Peoples R China Shanghai Univ Int Business & Econ Management Sch Shanghai Peoples R China Virginia Polytech Inst & State Univ Pamplin Coll Business Blacksburg VA 24061 USA Tongji Univ Sch Ocean & Earth Sci Shanghai Peoples R China

ISBN: (纸本)9781509034840

The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering automatically and removed the noise of image after classification. Meanwhile, as an alternative to expert knowledge, data fusion method was used, which included the fusion of aeromagnetic data, gravity data and elevation data. The empirical results showed that this method can avoid the highly dependent on domain knowledge experts in image recognition and got a better classification effect in remote sensing image.

关键词： fuzzy c-means algorithm remote sensing image image classification data fusion

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Interval-Valued fuzzy c-means algorithm and Interval-Valued Density-Based fuzzy c-means algorithm

Interval-Valued Fuzzy c-Means Algorithm and Interval-Valued ...

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IEEE International conference on fuzzy Systems (FUZZ-IEEE)

作者： Varshney, Ayush K. Mehra, Priyanka Muhuri, Pranab K. Lohani, Q. M. Danish South Asian Univ Dept Comp Sci New Delhi India

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169323

Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the membership value of the fuzzy set as an interval. IVFS assumes that membership interval can better represent the uncertainty in the data. Accordingly, IVFS can be used to obtain good clustering results since it can represent the uncertainty more appropriately. Thus, this paper proposes the interval-valued fuzzy c-means algorithm (IVFcM) which uses IVFSs to represent the data. The concept of the proposed IVFcM is then extended to introduce the interval-valued density based fuzzy c-means (IVDFcM) algorithm based on the distance measure of IVFSs. Both IVFcM and IVDFcM are simulated over various UcI benchmark datasets to show their suitability and supremacy over their existing counterparts.

关键词： Interval-valued fuzzy set fuzzy c-means algorithm density-based c-means algorithm IVFS distance measure

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Improvement on A fuzzy c-means algorithm based on genetic algorithm

Improvement on A fuzzy c-means algorithm based on genetic al...

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International conference on Machinery, Electronics and control Simulation (IcMEcS)

作者： Jiang, Guo chen Sun, Zhijian Air Force Early Warning Acad Wuhan Wuhan Peoples R China

ISBN: (纸本)9783038352105

Weighting exponent m is an important parameter in fuzzy c-means(FcM) algorithm. In this paper, an approach based on genetic algorithm is proposed to improve the FcM clustering algorithm through the optimal choice of the parameter m. Experimental results show that the better clustering results are obtained through the new algorithm.

关键词： weighting exponent fuzzy c-means algorithm clustering analysis genetic algorithm

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clonal Selection based fuzzy c-means algorithm for clustering 14

Clonal Selection based Fuzzy C-Means Algorithm for Clusterin...

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16th Genetic and Evolutionary computation conference (GEccO)

作者： Ludwig, Simone A. N Dakota State Univ Dept Comp Sci Fargo ND 58105 USA

ISBN: (纸本)9781450326629

In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialization of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (cSFcM) is introduced. cSFcM is compared with the basic fuzzy c-means (FcM) algorithm, a genetic algorithm based FcM (GAFcM) algorithm, and a particle swarm optimization based FcM (PSOFcM) algorithm.

关键词： Evolutionary computation fuzzy c-means algorithm data clustering

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A Rough-set based Measurement for the Membership Degree of fuzzy c-means algorithm 3

A Rough-set based Measurement for the Membership Degree of F...

引用

3rd International Workshop on Pattern Recognition

作者： Wang, Zhenhao Fan, Jiancong Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Shandong Univ Sci & Technol Prov Expt Teaching Demonstrat Ctr Comp Qingdao Peoples R China

ISBN: (数字)9781510622692

ISBN: (纸本)9781510622692

The traditional fuzzy c-means (FcM) algorithm is stable and easy to be implemented. However, the data elements in the cluster boundary of FcM are easily clustered into incorrect classes making the efficiency of FcM algorithm reduced. Aiming at solving this problem, this paper presents a Rough-FcM algorithm which is combined FcM algorithm with rough set according to new equations. We take the advantage of the positive region set and the boundary region set of rough set. First, Rough-FcM algorithm divides the data elements into the positive region set or the boundary region set of all classes according to the threshold we set. Second, it updates the cluster centers and membership matrixes with new equations. Thus, we can execute the second clustering based on first clustering of FcM. By comparing the experimental results of the Rough-FcM with K-means, DBScAN and FcM according to four clustering evaluation indexes on both synthetic and real datasets, we evaluate our proposed algorithm and improve outcomes from most of datasets by adopting these three classic clustering algorithms mentioned above.

关键词： fuzzy c-means algorithm rough set positive region boundary region threshold clustering

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Image Segmentation using fuzzy c-means algorithm Incorporating Weighted Local complement Membership and Local Data Distances

Image Segmentation using Fuzzy C-means Algorithm Incorporati...

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World Symposium on computer Applications and Research (WScAR)

作者： Gharieb, R. R. Gendy, G. Abdelfattah, A. Assiut Univ Fac Engn Assiut 71516 Egypt Assiut Univ El Rajhy Liver Hosp Assiut Egypt

ISBN: (纸本)9781509041145

fuzzy c-means (FcM) algorithm is widely used for unsupervised image segmentation. However, the FcM algorithm does not take into account the local information in the image context. This makes the FcM algorithm sensitive to additive noise degrading the image pixels features. In this paper, an approach to incorporating local data context and membership information into the FcM is presented. The approach consists of adding a weighted regularization function to the standard FcM algorithm. This function is formulated to resemble the standard FcM objective function but the distance is replaced by a new one generated from the local complement or residual membership. The applied regularizing weight is a constant weight or alternatively an adaptive one. The adaptive weight is the Euclidian distance between the center prototype and the local image data mean. The regularizing function aims at smoothing out additive noise and biasing the clustered image to piecewise homogenous regions. Simulation results of clustering and segmentation of synthetic and real-world noisy images have been presented. These results have shown that the presented approach enhances the performance of the FcM algorithm in comparison with the standard FcM and several previously modified FcM algorithms.

关键词： Image clustering K-means algorithm Soft clustering fuzzy c-means algorithm Spatial data and membership information based FcMs

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Detection of Suspicious Lesions in Mammogram using fuzzy c-means algorithm 5

Detection of Suspicious Lesions in Mammogram using Fuzzy C-M...

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International conference on Advances in computing, communications and Informatics (IcAccI)

作者： Kumar, Mukesh Thakkar, V. M. Bhatt, Upendra Soliyal, Neema GBPEC Pauri 246194 Uttarakhand India GBPEC CSED Pauri 246194 Uttarakhand India

ISBN: (纸本)9781509020287

Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is used, which is an X-ray inspection of the breast, which can be used to detect the breast tumor which may lead to breast cancer. Using Mammography, a small lump that may lead to breast cancer can be detected at the initial stage. Sometimes it is not possible to recognize very small tumors because of noisy, blurred, and fuzzy images. Therefore, they need to be enhanced to increase the contrast for better visual perception and reduce the noise from it for better diagnosis. In this work, FcM algorithm is used to detect the suspicious lesions in a mammogram. To achieve the objective of this work, MIAS (Mammographic Image Analysis Society) and INbreast databases are used, which contain 322 and 412 images of the breast (both left and right breast) respectively. In these databases, every image is examined by the expert radiologists. The effectiveness of the algorithm is measured in terms of MSE and PSNR.

关键词： Mammograms Breast cancer Wavelet Denoising Filter fuzzy c-means algorithm Morphological Operation Suspicious Lesions

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