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Secure weighted possibilistic c-means algorithm on cloud for clustering big data

INFORMATION ScIENcES 2019年 479卷 515-525页

作者： Zhang, Qingchen Yang, Laurence T. castiglione, Arcangelo chen, Zhikui Li, Peng Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS Canada Univ Salerno Dept Comp Sci Fisciano Italy Dalian Univ Technol Sch Software Technol Dalian Peoples R China

The weighted possibilistic c-means algorithm is an important soft clustering technique for big data analytics with cloud computing. However, the private data will be disclosed when the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud. Finally, the presented scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and scalability. The results show that the presented scheme performs more efficiently than the traditional weighted possiblistic c-means algorithm and it achieves a good scalability on cloud for big data clustering. (c) 2018 Elsevier Inc. All rights reserved.

关键词： Big data possibilistic c-means algorithm cloud computing BGV

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possibilistic Intuitionistic Fuzzy c-means clustering algorithm for MRI Brain Image Segmentation

Possibilistic Intuitionistic Fuzzy c-Means Clustering Algori...

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8th Panhellenic conference on Artificial Intelligence

作者： Verma, Hanuman Agrawal, R. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

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. possibilistic fuzzy c-means (PFcM) algorithm is the hybridization of fuzzy c-means (FcM) and possibilistic c-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 intuitionistic fuzzy c-means (PIFcM) algorithm for Atanassov's intuitionistic fuzzy 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.

关键词： possibilistic c-means algorithm fuzzy c-means algorithm A-IFS intuitionistic fuzzy c-means algorithm magnetic resonance imaging image segmentation

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possibilistic Intuitionistic Fuzzy c-means clustering algorithm for MRI Brain Image Segmentation

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INTERNATIONAL JOURNAL ON ARTIFIcIAL INTELLIGENcE TOOLS 2015年第5期24卷

作者： Verma, Hanuman Agrawal, R. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

关键词： possibilistic c-means algorithm fuzzy c-means algorithm A-IFS intuitionistic fuzzy c-means algorithm magnetic resonance imaging image segmentation

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possibilistic c-means with novel image representation for image segmentation

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ARTIFIcIAL INTELLIGENcE REVIEW 2024年第2期58卷 1-21页

作者： cui, Hanshuai Wang, Hongjian Zeng, Wenyi Liu, Yuqing Zhao, Bo Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China State Grid Henan Prov Elect Power Co Management Training Ctr Zhengzhou 450015 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. compared to the traditional Fuzzy c-means algorithm, the possibilistic c-means (PcM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PcM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based possibilistic c-means (VBPcM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPcM algorithm outperforms the conventional PcM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPcM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.

关键词： possibilistic c-means algorithm clustering algorithm Image denoising Image representation method Image segmentation

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A Fuzzy support vector classifier based on Bayesian optimization

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FUZZY OPTIMIZATION AND DEcISION MAKING 2008年第1期7卷 75-86页

作者： Zhang, Yong chi, Zhong-Xian Dalian Univ Technol Dept Comp Sci & Engn Dalian 116024 Peoples R China Liaoning Normal Univ Dept Comp Dalian 116029 Peoples R China

In this paper, we have focused on the use of the support vector data description based on kernel-based possibilistic c-means algorithm (PcM) for solving multi-class classification problems. We propose a weighted support vector data description (SVDD) multi-class classification method, which can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the kernel-based PcM method. Accordingly, this paper presents the multi classification algorithm and gives the simple classification rule, which satisfies Bayesian optimal decision theory. With a simple classification rule, our experimental results show that the proposed method can reduce the effect of outliers and reduce the rate of classification error.

关键词： support vector machine support vector data description possibilistic c-means algorithm kernel method Bayesian optimization

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Fuzzy multi-class classifier based on support vector data description and improved PcM

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EXPERT SYSTEMS WITH APPLIcATIONS 2009年第5期36卷 8714-8718页

作者： Zhang, Yong chi, Zhong-Xian Li, Ke-Qiu Liaoning Normal Univ Dept Comp Dalian 116081 Liaoning Prov Peoples R China Dalian Univ Technol Dept Comp Sci & Engn Dalian 116024 Peoples R China

in this paper, a novel fuzzy classifier for multi-classification problems, based on support vector data description (SVDD) and improved PcM, is proposed. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the improved PcM method. Accordingly, this paper presents the multi-classification algorithm based on the robust weighted SVDD, and gives the simple classification rule. Experimental results show that the proposed method can reduce the effect of outliers and yield higher classification rate. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Support vector data description possibilistic c-means algorithm Minimum enclosing sphere classifier SVM

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Improvement of cSF, WM and GM Tissue Segmentation by Hybrid Fuzzy - possibilistic clustering Model based on Genetic Optimization case Study on Brain Tissues of Patients with Alzheimer's Disease

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INTERNATIONAL JOURNAL OF NETWORKED AND DISTRIBUTED cOMPUTING 2018年第2期6卷 63-77页

作者： Lazli, Lilia Boukadoum, Mounir Univ Quebec UQAM Dept Comp Sci CoFaM Res Ctr Ste Foy PQ Canada Univ Quebec ETS Dept Elect Engn Ste Foy PQ Canada

Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy c-mean (FcM) is one of the most popular clustering based segmentation methods. However FcM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(cSF) tissues using hybrid clustering process which based on: (1) FcM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) possibilistic c-means (PcM) algorithm for volumetric measurements of WM, GM, and cSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer's disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FcM and PcM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.

关键词： Fuzzy c-means algorithm possibilistic c-means algorithm Genetic algorithms Hybrid reasoning Brain tissue clustering Alzheimer's disease

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Brain Tissue classification of Alzheimer Disease Using Partial Volume possibilistic Modeling: Application to ADNI Phantom Images 7

Brain Tissue Classification of Alzheimer Disease Using Parti...

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7th International conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Lazli, Lilia Boukadoum, Mounir Ait-Mohamed, Otmane ETS Dept Elect Engn Montreal PQ Canada Univ Quebec Montreal Dept Comp Sci CoFaM Res Ctr Montreal PQ Canada Concordia Univ Dept Elect Engn & Comp Sci Montreal PQ Canada

ISBN: (纸本)9781538618424

This paper describes an automatic segmentation approach for PET and T1-weighted MR images using a possibilistic clustering algorithm for deriving fuzzy tissue maps of white matter, gray matter and cerebrospinal fluid volumes, and using the fuzzy c-means algorithm for the centers initialization process;this hybrid technique allows to compute the degree of membership of each voxel to different brain tissues. The fuzzy process is illustrated for Alzheimer's disease using phantom images from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our method, inspired from the conventional possibilistic algorithm, is less sensitive to noise while taking into consideration the effect of partial volume.

关键词： Alzheimer disease Brain tissue clustering Fuzzy c-means algorithm possibilistic c-means algorithm ADNI database

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