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DwfwFcm: An effective fuzzy c-means clustering framework considering the different data weights and feature weights

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2019年第3期37卷 4339-4347页

作者： Wu, Ziheng Wang, Bing AnHui Univ Technol Sch Elect & Informat Engn Maanshan 243000 Peoples R China

fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and the discriminative ability of the different features in the clustering process. In this paper, we propose a new kind of Fcm clustering framework: *** the different data weights and feature weights, an adaptive data weights vector and an adaptive feature weights matrix are introduced into the conventional Fcm and a new objective function is constructed. By the proposed objective function, the corresponding scientific updating iterative rules of the membership matrix, the weights of the different feature, the weights of the different data object and the cluster centers can be derived *** results have demonstrated that the algorithm proposed in this paper can deliver consistently promising results and improve the clustering performance greatly.

关键词： fuzzy c-means algorithm machine learning clustering adaptive data weights vector adaptive feature weights vector

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An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT

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FUTURE GENERATION cOMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF EScIENcE 2018年 88卷 675-682页

作者： Bu, Fanyu Inner Mongolia Univ Finance & Econ Coll Comp & Informat Management Hohhot Peoples R China

Mining smart data from the collected big data in Internet of Things which attempts to better human life by integrating physical devices into the information space. As one of the most important clustering techniques for drilling smart data, the fuzzy c-means algorithm (FcM) assigns each object to multiple groups by calculating a membership matrix. However, each big data object has a large number of attributes, posing an remarkable challenge on FcM for loT big data real-time clustering. In this paper, we propose an efficient fuzzy c-means approach based on the tensor canonical polyadic decomposition for clustering big data in Internet of Things. In the presented scheme, the traditional fuzzy c-means algorithm is converted to the high-order tensor fuzzy c-means algorithm (HOFcM) via a bijection function. Furthermore, the tensor canonical polyadic decomposition is utilized to reduce the attributes of every objects for enhancing the clustering efficiency. Finally, the extensive experiments are conducted to compare the developed scheme with the traditional fuzzy c-means algorithm on two large loT datasets including sWSN and eGSAD regarding clustering accuracy and clustering efficiency. The results argue that the developed scheme achieves a significantly higher clustering efficiency with a slight clustering accuracy drop compared with the traditional algorithm, indicating the potential of the developed scheme for drilling smart data from loT big data. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Big data Internet of Things Smart data fuzzy c-means algorithm canonical polyadic decomposition

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Support vector classifier based on fuzzy c-means and Mahalanobis distance

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2010年第2期35卷 333-345页

作者： Zhang, Yong Xie, Fuding Huang, Dan Ji, Min Liaoning Normal Univ Dept Comp Dalian 116081 Liaoning Prov Peoples R China

This paper presents a fuzzy support vector classifier by integrating modified fuzzy c-means clustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithm can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The modified fuzzy c-means clustering algorithm based on Mahalanobis distance takes into the samples' correlation account, and is improved to generate different weight values for main training data points and outliers according to their relative importance in the training data. Experimental results show that the proposed method can reduce the effect of outliers and give high classification accuracy.

关键词： Support vector data description fuzzy c-means algorithm Mahalanobis distance Support vector machine

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Novel local information kernelized fuzzyc-means algorithm for image segmentation

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TEcHNOLOGY 2021年第2期31卷 786-801页

作者： Li, Songcheng Lu, Junyong cheng, Long Li, Xiangping Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan 430033 Hubei Peoples R China

In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present a local information kernelized fuzzy c-means (LIKFcM) algorithm for image segmentation. The dissimilarity measure metric, considering an adaptive tradeoff weighted factor, incorporates the Mahalanobis distance and outliers-rejection-based spatial term which eliminates unreliable neighboring information. By using this dissimilarity measure metric, the new algorithm could take reliable contextual information into account and achieve better segmentation results on images with complexed boundaries. Furthermore, the adaptive tradeoff factor depends on a fast noise estimation algorithm. This factor avoids subjective adjustment and makes the LIKFcM algorithm more universal. To evaluate the performance of the proposed algorithm both quantitatively and qualitatively, experiments are conducted both on synthetic images and real-world images with different kinds of noise. Segmentation Accuracy (SA) and comparison scores are used to evaluate the performance of both proposed algorithm and other methods. Experimental results illustrate that the proposed algorithm has better performance on denoising and reserving useful edges. The LIKFcM algorithm not only shows more robustness to noise but also preserves the texture details of the images.

关键词： contextual information fuzzy c-means algorithm image segmentation novel dissimilarity metric

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Application of fuzzy c-means clustering method in the analysis of severe medical images

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第4期38卷 3635-3645页

作者： Ning, Yuwen Shi, Xiaoyuan Yin, Jingong Xie, Duowen Fourth Mil Med Univ Dept Mil Prevent Med Xian Shaanxi Peoples R China Lanzhou Univ Second Hosp Dept Med Record Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept Intens Care Unit Med Record2 Lanzhou Gansu Peoples R China

Medical image processing is an interdisciplinary subject of integrated medical imaging, mathematics, computer science and other disciplines. With high spatial resolution, high signal-to-noise ratio and high resolution of soft tissue, the technology can accurately locate the target areas of interest in medical images, thus providing useful information for clinicians to formulate disease treatment plans. These techniques include digital subtraction angiography, magnetic resonance imaging, computed tomography, ultrasound imaging and positron emission tomography. The purpose of this paper is to study the application of fuzzy c-means clustering in image analysis of critical medicine. This paper discusses the classification effect, clustering process, iteration times and running time of different algorithms, and the segmentation effect of different algorithms. By designing parameters and carrying out simulation experiments, the traditional clustering algorithm and improved local adaptive method are compared, and the problem of long coding time of traditional image compression algorithm is solved. The simulation results under the same working environment show that the coding speed of the algorithm is about five times faster than that of the traditional image compression algorithm without affecting the signal-to-noise ratio and compression rate, which proves the superiority of the algorithm.

关键词： Medical image processing fuzzy c-means algorithm clustering algorithm medical image analysis

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fuzzy c-means based data envelopment analysis for mitigating the impact of units' heterogeneity

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KYBERNETES 2016年第3期45卷 536-551页

作者： Hajiagha, Seyed Hossein Razavi Hashemi, Shide Sadat Mahdiraji, Hannan Amoozad Khatam Inst Higher Educ Dept Management Tehran Iran Allameh Tabatabaei Univ Fac Management & Accounting Tehran Iran Islamic Azad Univ Dept Management Kashan Branch Kashan Iran

Purpose - Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach - To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy c-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy c-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings - Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications - The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value - The paper proposed a hybrid algorithm combination of fuzzy c-means clustering method with classic DEA models for the first time.

关键词： cluster analysis Data envelopment analysis fuzzy c-means algorithm Heterogeneous units

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New inter-cluster proximity index for fuzzy c-means clustering

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IEIcE TRANSAcTIONS ON INFORMATION AND SYSTEMS 2008年第2期E91D卷 363-366页

作者： Li, Fan Dai, Shijin Liu, Qihe Yang, Guowei Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610051 Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 610051 Peoples R China

This letter presents a new inter-cluster proximity index for fuzzy partitions obtained from the fuzzy c-means algorithm. It is defined as the average proximity of all possible pairs of clusters. The proximity of each pair of clusters is determined by the overlap and the separation of the two clusters. The former is quantified by using concepts of fuzzy Rough sets theory and the latter by computing the distance between cluster centroids. Experimental results indicate the efficiency of the proposed index.

关键词： fuzzy c-means algorithm fuzzy rough sets inter-cluster proximity cluster validity fuzzy clustering

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Performance based analysis between k-means and fuzzy c-means clustering algorithms for connection oriented telecommunication data

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APPLIED SOFT cOMPUTING 2014年 19卷 134-146页

作者： Velmurugan, T. DG Vaishnav Coll PG & Res Dept Comp Sci Madras 600106 Tamil Nadu India

Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-means and fuzzy c-means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in fuzzy c-means algorithm than the k-means. (c) 2014 Elsevier B.V. All rights reserved.

关键词： k-means algorithm fuzzy c-means algorithm Data clustering Data analysis Telecommunication data

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Unsupervised change detection using a novel fuzzy c-means clustering simultaneously incorporating local and global information

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MULTIMEDIA TOOLS AND APPLIcATIONS 2017年第19期76卷 20081-20098页

作者： Hao, Ming Hua, Zhang Li, Zhenxuan chen, Bingqian China Univ Mining & Technol Sch Environm Sci & Spatial Informat Daxue Rd 1 Xuzhou 221116 Peoples R China Jiangsu Normal Univ Sch Geog Geomat & Urban Rural Planning Xuzhou Peoples R China

This paper presents a novel fuzzy c-means (FcM) clustering simultaneously incorporating local and global information (FLGIcM) method to unsupervised change detection (cD) from remotely sensed images. A new factor including three local, global and edge parameters is added into the conventional FcM to enhance the insensitivity to noise and preserve detailed features. The spatial attraction between the central pixel and its neighborhood pixels is incorporated as a local parameter to utilize spatial information. A global parameter designed based on the estimated mean values of changed and unchanged pixels is introduced into the new factor to enhance its robustness and ability of separating changed from unchanged pixels. In addition, an edge parameter is also added to remain accurate edges and change details. Two experiments were carried out on Landsat images to test the performance of FLGIcM. Experimental results indicate that FLGIcM always achieves high accuracy and overperforms some state-of-the-art cD methods. Therefore, the proposed FLGIc provides an effective unsupervised cD method.

关键词： Remote sensing change detection fuzzy c-means algorithm Local information Spatial attraction Global information

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Extractive text summarization of arabic multi-document using fuzzy c-means and Latent Dirichlet Allocation

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANcE ENGINEERING AND MANAGEMENT 2024年第2期15卷 713-726页

作者： Al-Taani, Ahmad T. T. Al-Sayadi, Sami H. H. Yarmouk Univ Dept Comp Sci Irbid Jordan

In this research, we investigated the performance of the combination of fuzzy c-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.

关键词： Multi-document text summarization Arabic Language Extractive-based summarization Singular value decomposition (SVD) fuzzy c-means algorithm Latent Dirichlet allocation (LDA) algorithm

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