fuzzyc-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affectedby its equal partition trend for data set of large discrepancy of each class samples number, and th...
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ISBN:
(纸本)9780819469526
fuzzyc-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affected
by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering
result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian
function Weighted fuzzyc-means (WFcM) algorithm is proposed, which the weighted function is produced by a
Gaussian function calculating dot density of each sample. To certain extent, the WFcM algorithm has not only overcome
the limitation of equal partition trend in fuzzycmeansalgorithm, but also been favorable convergence and stability. The
calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are
both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFcM
algorithm, the classification performance of the WFcM algorithm is further enhanced and the convergent speed of
objective function is further accelerated.
Suppressed fuzzyc-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 clas...
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ISBN:
(纸本)3540882685
Suppressed fuzzyc-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 fuzzyc-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.
Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzyc-means (FcM) or similar clustering mechanisms. S...
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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 fuzzyc-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.
Suppressed fuzzyc-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzyc-meansclustering algorithm. Patt. Recogn. Lett. 24, 1607-1612 (2003)] with the intention of combin...
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ISBN:
(纸本)9783540859192
Suppressed fuzzyc-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzyc-meansclustering 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 fuzzyc-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.
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 initi...
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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.
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 interv...
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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 fuzzyc-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.
Weighting exponent m is an important parameter in fuzzyc-means(FcM) algorithm. In this paper, an approach based on geneticalgorithm is proposed to improve the FcM clustering algorithm through the optimal choice of t...
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ISBN:
(纸本)9783038352105
Weighting exponent m is an important parameter in fuzzyc-means(FcM) algorithm. In this paper, an approach based on geneticalgorithm 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.
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 fuzzyclustering is that in hard...
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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 fuzzyclustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzyclustering each data point belongs to several clusters that are associated with a certain membership degree. fuzzyc-meansclustering 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 basicfuzzyc-means (FcM) algorithm, a geneticalgorithm based FcM (GAFcM) algorithm, and a particle swarm optimization based FcM (PSOFcM) algorithm.
The traditional fuzzyc-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 al...
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ISBN:
(数字)9781510622692
ISBN:
(纸本)9781510622692
The traditional fuzzyc-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 classicclustering algorithms mentioned above.
fuzzyc-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 sensitiv...
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ISBN:
(纸本)9781509041145
fuzzyc-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.
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