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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

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SOFT cOMPUTING 2010年第5期14卷 495-505页

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

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 Generalized Approach to the suppressed fuzzy c-means algorithm

A Generalized Approach to the Suppressed Fuzzy <i>c</i>-Mean...

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7th International conference on Modeling Decisions for Artificial Intelligence (MDAI)

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

ISBN: (纸本)9783642162916

suppressed fuzzy c-means (s-FcM) clustering was introduced with the intention of combining the higher convergence speed of hard c-means (HcM) clustering with the finer partition quality of fuzzy c-means (FcM) algorithm. Suppression modifies the FcM iteration by creating a competition among clusters: lower degrees of memberships are reduced via multiplication with a previously set constant suppression rate, while the largest fuzzy membership grows by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in terms of accuracy and working time. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Based on a large amount of numerical tests performed in multidimensional environment, some generalized forms of suppression proved to give more accurate partitions than FcM and s-FcM.

关键词： fuzzy c-means algorithm suppressed fuzzy c-means algorithm competitive clustering context sensitive suppression rules

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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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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 ...

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5th International conference on Modeling Decisions for Artificial Intelligence

ISBN: (纸本)3540882685

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

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Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation

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EXPERT SYSTEMS WITH APPLIcATIONS 2014年第9期41卷 4083-4093页

作者： Zhao, Feng Fan, Jiulun Liu, Hanqiang Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710061 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

suppressed fuzzy c-means clustering algorithm (S-FcM) is one of the most effective fuzzy clustering algorithms. Even if S-FcM has some advantages, some problems exist. First, it is unreasonable to compulsively modify the membership degree values for all the data points in each iteration step of S-FcM. Furthermore, duo to only utilizing the spatial information derived from the pixel's neighborhood window to guide the process of image segmentation, S-FcM cannot obtain satisfactory segmentation results on images heavily corrupted by noise. This paper proposes an optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation to solve the above drawbacks of S-FcM. Firstly, an optimal-selection-based suppressed strategy is presented to modify the membership degree values for data points. In detail, during each iteration step, all the data points are ranked based on their biggest membership degree values, and then the membership degree values of the top r ranked data points are modified while the membership degree values of the other data points are not changed. In this paper, the parameter r is determined by the golden section method. Secondly, a novel gray level histogram is constructed by using the self-tuning non local spatial information for each pixel, and then fuzzy c-means clustering algorithm with the optimal-selection-based suppressed strategy is executed on this histogram. The self-tuning non local spatial information of a pixel is derived from the pixels with a similar neighborhood configuration to the given pixel and can preserve more information of the image than the spatial information derived from the pixel's neighborhood window. This method is applied to Berkeley and other real images heavily contaminated by noise. The image segmentation experiments demonstrate the superiority of the proposed method over other fuzzy algorithms. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Image segmentation fuzzy clustering suppressed fuzzy c-means algorithm Non local spatial information Self-tuning strategy

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