fuzzyc-means (FcM) clustering has been widely used successfully in many real-world applications. However, the FcM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters...
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fuzzyc-means (FcM) clustering has been widely used successfully in many real-world applications. However, the FcM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters. In this paper, a multi-center fuzzy c-means algorithm based on transitive closure and spectral clustering (MFcM-TcSc) is provided. In this algorithm, the initial guesses of the locations of the cluster centers or the membership values are not necessary. Multi-centers are adopted to represent the non-spherical shape of clusters. Thus, the clustering algorithm with multi-center clusters can handle non-traditional curved clusters. The novel algorithmcontains three phases. First, the dataset is partitioned into some subclusters by FcM algorithm with multi-centers. Then, the subclusters are merged by spectral clustering. Finally, based on these two clustering results, the final results are obtained. When merging subclusters, we adopt the lattice similarity method as the distance between two subclusters, which has explicit form when we use the fuzzy membership values of subclusters as the features. Experimental results on two artificial datasets, UcI dataset and real image segmentation show that the proposed method outperforms traditional FcM algorithm and spectral clustering obviously in efficiency and robustness. (c) 2013 Elsevier B.V. All rights reserved.
Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can le...
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Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilisticfuzzyc-means (PFcM) algorithm is the hybridization of fuzzyc-means (FcM) and possibilisticc-means (PcM) algorithms which overcomes the problem of noise in the FcM algorithm and coincident clusters problem in the PcM algorithm. A major challenge posed in the PFcM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionisticfuzzyc-means (PIFcM) algorithm for Atanassov's intuitionisticfuzzy sets (A-IFS) which includes the advantages of the PcM, FcM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFcM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.
The aim of this study was to establish a multi-stage fuzzyc-means (FcM) framework for the automatic and accurate detection of brain tumors from multimodal 3D magnetic resonance image data. The proposed algorithm uses...
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ISBN:
(纸本)9781467376822
The aim of this study was to establish a multi-stage fuzzyc-means (FcM) framework for the automatic and accurate detection of brain tumors from multimodal 3D magnetic resonance image data. The proposed algorithm uses prior information at two points of the execution: (1) the clusters of voxels produced by FcM are classified as possibly tumorous and non-tumorous based on data extracted from train volumes;(2) the choice of FcM parameters (e.g. number of clusters, fuzzy exponent) is supported by train data as well. FcM is applied in two stages: the first stage eliminates the most part of non-tumorous tissues from further processing, while the second stage is intended to accurately extract the tumor tissue clusters. The algorithm was tested on six selected volumes from the BRATS 2012 database. The achieved accuracy is generally characterized by a Dice score in the range of 0.7 to 0.9. Tests have revealed that increasing the size of the train data set slightly improves the overall accuracy.
Discontinuities have huge impact on civil and mining engineering. To understand the spatial features of discontinuities, it is common to group them into different sets based on orientation. In this paper, a new algori...
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Discontinuities have huge impact on civil and mining engineering. To understand the spatial features of discontinuities, it is common to group them into different sets based on orientation. In this paper, a new algorithm is introduced for the identification of discontinuity sets. The new algorithm is developed by combined fuzzy c-means algorithm with variable length string geneticalgorithm. In the new method, the number of discontinuity sets is not the necessary input parameter any more. This method is robust, global optimal and totally automatic. To verify its validity, the new method was firstly applied to an artificial data as well as a published data. For artificial data set, the assignment error rate is only 7.4%. For published data set, only 2 discontinuities are assigned to wrong sets. The results indicate that the new algorithm is better than fuzzy c-means algorithm and comparable with other common methods. Afterwards, the new method was utilized to analyze the orientation data sampled at an underground storage cavern site. The new method determines that the ideal number of sets is 3. The new method provided satisfactory results, which confirm its effectiveness and convenience.
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.
In this research, we investigated the performance of the combination of fuzzyc-means and latent Dirichlet allocation algorithms for Arabic multi-document summarization. The summary should include the most essential s...
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In this research, we investigated the performance of the combination of fuzzyc-means and latent Dirichlet allocation algorithms for Arabic multi-document summarization. The summary should include the most essential sentences from multi-documents with the same topic. The TAc-2011 corpus is used for experiments, first, the documents in the corpus are clustered using fuzzy c-means algorithm. The aim of the clustering process here is to classify the documents according to their topics, e.g., economic, politic, sport, etc. The results are compared against some recent Arabic summarization approaches that used ant colony and discriminant analysis algorithms. The proposed approach has obtained competitive results compared to those recent approaches.
A video recommendation framework for e-commerce clients is proposed using the collaborative filtering (cF) process. One of the most important features of the cF algorithm is its scalability. To avoid the issue, a hybr...
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A video recommendation framework for e-commerce clients is proposed using the collaborative filtering (cF) process. One of the most important features of the cF algorithm is its scalability. To avoid the issue, a hybrid model-based collaborative filtering approach is proposed. KL Divergence was developed to address the cF technique's scalability problem. The clustering with enhanced sqrt-cosine similarity Recommender scheme is proposed. For successful clustering, Kullback-Leibler Divergence-based fuzzyc-meansclustering is suggested, with the aim of focusing on greater accuracy during movie *** proposed scheme is viewed as a trustworthy contribution that significantly improves the ability of movie recommendation by virtue of the KL divergence-based fuzzyc-meansclustering mechanism and enhanced sqrt-cosine similarity. The proposed scheme highlighted and addressed the critical role of the KL divergence-based cluster ensemble factor in improving clustering stability and robustness. For prediction, the enhanced sqrt-cosine similarity was used to calculate successful related neighbor users. The performance of Recommendation is improved when KLD-FcM is combined with improved sqrt-cosine *** proposed scheme's empirical work on the Movielens dataset in terms of MAE, RMSE, SD, and Recall were found to be superior in recommendation accuracy compared to traditional approaches and some non-clustering based methods recommended for study. With the specified number of clusters, it is capable of providing accurate and customized movie recommendation systems.
The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient...
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The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient use of limited resources and appropriate routing of the data packets. The power consumption can be minimized by balancing the energy consumption between the sensor nodes and selecting the minimum power consumption route for the data packets. clustering is one of the most effective technique that not only uniformly distributes the energy among all the sensor nodes but also play a vital role in the designing of routing protocols. So based on these advantages, a low power consumption routing protocol is proposed that makes use of fuzzyc-means++ algorithm. The proposed approach minimizes the power consumption of the sensor network by the excellent management of the WSN and also raises the lifespan. The simulation result illustrates the effectiveness of the proposed routing method when compared with the recently developed protocols based on k-means and fuzzy c-means algorithms.
Nearshore ship detection remains a challenging task due to the similarity in grayscale between harbor areas and ships. In this paper, we propose a new superpixel-based nearshore ship detection method, which effectivel...
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ISBN:
(纸本)9798350360332;9798350360325
Nearshore ship detection remains a challenging task due to the similarity in grayscale between harbor areas and ships. In this paper, we propose a new superpixel-based nearshore ship detection method, which effectively reduces land false alarms by the fuzzyc-means (FcM) algorithm. First, the method applies the simple linear iterative clustering (SLIc) algorithm to generate superpixel regions. Superpixels can preserve the boundary of the target and reduce the effects of speckle noise for target detection in synthetic aperture radar (SAR) images. Subsequently, we used the FcM algorithm to quantify the statistical differences between different superpixels, and the clustering results can serve as an indicative measure of the probability that each superpixel belongs to the sea category. Finally, we incorporate this probability into our proposed saliency detection method, facilitating the efficient identification of the ship regions. The experimental results show that the method can robustly and efficiently detect nearshore ship targets.
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