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A novel fuzzy c-means algorithm to generate diverse and desirable cluster solutions used by genetic-based clustering ensemble algorithms

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MEMETIc cOMPUTING 2012年第1期4卷 49-71页

作者： Ghaemi, Reza Sulaiman, Md Nasir Ibrahim, Hamidah Mustapha, Norwati Univ Putra Malaysia Dept Comp Sci Fac Comp Sci & Informat Technol Serdang Malaysia

One of the most significant discussions in the field of machine learning today is on the clustering ensemble. The clustering ensemble combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known for their high ability to solve optimization problems, especially the problem of the clustering ensemble. To date, despite the major contributions to find consensus cluster partitions with application of genetic algorithms, there has been little discussion on population initialization through generative mechanisms in genetic-based clustering ensemble algorithms as well as the production of cluster partitions with favorable fitness values in first phase clustering ensembles. In this paper, a threshold fuzzy c-means algorithm, named TFcM, is proposed to solve the problem of diversity of clustering, one of the most common problems in clustering ensembles. Moreover, TFcM is able to increase the fitness of cluster partitions, such that it improves performance of genetic-based clustering ensemble algorithms. The fitness average of cluster partitions generated by TFcM are evaluated by three different objective functions and compared against other clustering algorithms. In this paper, a simple genetic-based clustering ensemble algorithm, named SGcE, is proposed, in which cluster partitions generated by the TFcM and other clustering algorithms are used as the initial population used by the SGcE. The performance of the SGcE is evaluated and compared based on the different initial populations used. The experimental results based on eleven real world datasets demonstrate that TFcM improves the fitness of cluster partitions and that the performance of the SGcE is enhanced using initial populations generated by the TFcM.

关键词： fuzzy c-means algorithm clustering ensemble Genetic-based clustering ensemble Genetic algorithms Diversity of clustering clustering accuracy

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A cloud and cloud Shadow Detection Method Based on fuzzy c-means algorithm

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IEEE JOURNAL OF SELEcTED TOPIcS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 1714-1727页

作者： Ping Bo Su Fenzhen Meng Yunshan Tianjin Univ Inst Surface Earth Syst Sci Tianjin 300072 Peoples R China Univ Chinese Acad Sci LREIS Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Natl Marine Data & Informat Serv Tianjin 300171 Peoples R China

cloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. In fuzzy clustering, the data points can belong to two or more clusters;hence, fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FcM), one typical fuzzy clustering method, was utilized in this study for cloud and cloud shadow detection. In addition, the "flood-fill" morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes including 27 Landsat ETM+ images in eight latitude zones and 14 Landsat OLI images comprising seven land cover types, including barren, forest, grass, shrubland, urban, water, and wetlands areas, with percentages of cloud cover from 4.99% to 97.63%, were utilized to confirm the validity of the FcM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FcM. compared with the function of mask (Fmask) method, the FcM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels;compared with the support vector machine (SVM) method, the FcM can achieve better cloud detection accuracy. The results demonstrate that the FcM can attain

关键词： cloud and cloud shadow detection fuzzy c-means algorithm multiple features multispectral sensors

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GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation

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MULTIMEDIA TOOLS AND APPLIcATIONS 2018年第16期77卷 21221-21243页

作者： Ali, Noureddine Ait cherradi, Bouchaib El Abbassi, Ahmed Bouattane, Omar Youssfi, Mohamed Hassan 2 Univ Casablanca UH2C ENSET SSDIA Lab Mohammadia Morocco Chouaib Doukkali Univ UCD Fac Sci CRMEF El Jadida El Jadida Morocco Chouaib Doukkali Univ UCD Fac Sci LaROSERI Lab El Jadida Morocco MIU FST EPIM Lab Errachidia Morocco

Image segmentation in the medical imagery such as MRI, is an essential step to the sensitive analysis of human tissues lesions with the objective to improve the partition of different parts of the image according to their specific characteristics. fuzzy c-means (FcM) is one of the widely used algorithms in literature regarding image segmentation. Indeed, it offers performances to the precision level in many medical fields of applications. However, the main limitation of FcM algorithm is time consuming during the image segmentation by clustering. In order to improve and to reduce the time delay of image data processing, we implemented three methods inspired from the FcM on GPU GT 740 m by using the cUDA environment. This latter is well adapted to the new architectures of processing, and its sequential migration towards the parallel approach through the SIMD architecture as GPU cards solves this time constraint. Furthermore, we have improved, via the two current developed implementations methods, the speed up of the processing acquisition in comparison with the reference methods. The efficiency evaluation such as strengths and weaknesses of each implemented method will be evaluated on medical images segmentation according to the size of the modelled brain tumours.

关键词： Image segmentation fuzzy c-means algorithm Parallel implementation GPU

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A Preferential Interval-Valued fuzzy c-means algorithm for Remotely Sensed Imagery classification

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INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2019年第7期21卷 2212-2222页

作者： Feng, Guozheng Ni, Mengying Ou, Shifeng Yan, Weiqing Xu, Jindong Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Yantai Univ Sch Optoelect Informat Sci & Technol Yantai 264005 Peoples R China

Remotely sensed imagery classification have a large amount of uncertainty related to the intraclass heterogeneity and the interclass ambiguity of objects. fuzzy set theory can address the uncertainty effectively, while interval-valued model can improve the separability of samples. Therefore, we propose a novel interval-valued fuzzy c-means algorithm, which integrates the intervalvalued model and preferential adaptive method. It preferentially adjusts the interval width according to MSE (mean-square-error) and boundary factor for determining the optimal set of features for the data. In this paper, it is proved that the method can make the intraclass MSE and boundary factor always proportional to the separability of objects, so that it can dynamically adjust the interval-valued separability by controlling the interval width. Experimental data consisting of SPOT5 (10-m spatial resolution) satellite data for three case study areas in china are used to test this algorithm. compared with other state-of-the-art fuzzy classification methods, our algorithm demonstrates the markedly improved overall accuracy and Kappa coefficients.

关键词： Remotely sensed classification Interval-valued model Preferential adaptive method fuzzy c-means algorithm

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11A robust active contour model driven by pre-fitting bias correction and optimized fuzzy c-means algorithm for fast image segmentation

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NEUROcOMPUTING 2019年 359卷 408-419页

作者： Jin, Ri Weng, Guirong Soochow Univ Sch Mech & Elect Engn 178 Ganjiang Rd Suzhou 215021 Jiangsu Peoples R China

Active contour model (AcM) is an effective method for image segmentation that has been widely used in various research fields. For images with severe intensity inhomogeneity, most existing AcMs show a poor segmentation performance. Moreover, robustness of these models to initial contour and noise is unsatisfactory. To seek better approaches to these issues, this paper proposes an AcM driven by pre-fitting bias correction and optimized fuzzy c-means (FcM) algorithm, which is robust and achieves a fast segmentation. Firstly, an optimized FcM algorithm is presented, by which bias field is pre-estimated. Secondly, a criterion function for local intensity is defined, then integrated with respect to the center for a global criterion. Thirdly, the above theory is introduced into AcM according to the property of level set function. Fourthly, a novel regularization method is proposed for variational level set. Experiments on real and synthetic images prove that the proposed model can effectively segment images with severe intensity inhomogeneity. compared with the bias correction model, there is no more time-consuming convolutions in iterations so that computational amount of our model is enormously reduced. Furthermore, the model has better robustness to both noise and initialization, segmentation efficiency and accuracy than most region-based models. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Active contour model Image segmentation Intensity inhomogeneity Bias correction fuzzy c-means algorithm Level set

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A THRESHOLDED fuzzy c-means algorithm FOR SEMI-fuzzy cLUSTERING

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PATTERN REcOGNITION 1991年第9期24卷 825-833页

作者： KAMEL, MS SELIM, SZ UNIV WATERLOO DEPT SYST DESIGN ENGNWATERLOO N2L 3G1ONTARIOCANADA KING FAHD UNIV PETR & MINERALS DEPT SYST ENGNDHAHRANSAUDI ARABIA

In this paper, the problem of achieving "semi-fuzzy" or "soft" clustering of multidimensional data is discussed. A technique based on thresholding the results of the fuzzy c-means algorithm is introduced. The proposed approach is analysed and contrasted with the soft clustering method (see S. Z. Selim and M. A. Ismail, Pattern Recognition 17, 559-568) showing the merits of the new method. Separation of clusters in the semi-fuzzy clustering context is introduced and the use of the proposed technique to measure the degree of separation is explained.

关键词： SOFT cLUSTERING SEMI-fuzzy cLUSTERING fuzzy c-means algorithm

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Product concept generation and selection using sorting technique and fuzzy c-means algorithm

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cOMPUTERS & INDUSTRIAL ENGINEERING 2006年第3期50卷 273-285页

作者： Yan, Wei chen, chun-Hsien Shieh, Meng-Dar Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Shanghai Maritime Univ Log Engn Sch Shanghai 200135 Peoples R China Natl Cheng Kung Univ Dept Ind Design Tainan 701 Taiwan

Product conceptualization is regarded as a key activity in new product development (NPD). In this stage, product concept generation and selection plays a crucial role. This paper presents a product concept generation and selection (PcGS) approach, which was proposed to assist product designers in generating and selecting design alternatives during the product conceptualization stage. In the PcGS, general sorting was adapted for initial requirements acquisition and platform definition;while a fuzzy c-means (FcM) algorithm was integrated with a design alternatives generation strategy for clustering design options and selecting preferred product concepts. The PcGS deliberates and embeds a psychology-originated method, i.e., sorting technique, to widen domain coverage and improve the effectiveness in initial platform formation. Furthermore, it successfully improves the FcM algorithm in such a way that more accurate clustering results can be obtained. A case study on a wood golf club design was used for illustrating the proposed approach. The results were promising and revealed the potential of the PcGS method. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： product concept generation and selection product platform design option sorting technique fuzzy c-means algorithm

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Reducing the time complexity of the fuzzy c-means algorithm

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2002年第2期10卷 263-267页

作者： Kolen, JF Hutcheson, T Univ W Florida Inst Human & Machine Cognit Pensacola FL 32501 USA

In this paper, we present an efficient implementation of the fuzzy c-means clustering algorithm. The original algorithm alternates between estimating centers of the clusters and the fuzzy membership of the data points. The size of the membership matrix is on the order of the original data set, a prohibitive size if this technique is to be applied to very large data sets with many clusters. Our implementation eliminates the storage of this data structure by combining the two updates into a single update of the cluster centers. This change significantly affects the asymptotic runtime as the new algorithm is linear with respect to the number of clusters, while the original is quadratic. Elimination of the membership matrix also reduces the overhead associated with repeatedly accessing a large data structure. Empirical evidence is presented to quantify the savings achieved by this new method.

关键词： clustering methods complexity theory fuzzy c-means algorithm

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Study on multi-center fuzzy c-means algorithm based on transitive closure and spectral clustering

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APPLIED SOFT cOMPUTING 2014年第0期16卷 89-101页

作者： Zeng, Shan Tong, Xiaojun Sang, Nong Wuhan Polytech Univ Coll Math & Comp Sci Wuhan 430023 Hubei Peoples R China Wuhan Text Univ Coll Math & Comp Sci Wuhan 430077 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Hubei Peoples R China

fuzzy c-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 algorithm contains 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.

关键词： fuzzy c-means algorithm Multi-center Lattice similarity Spectral clustering

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Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, china, using principal component analysis and an improved fuzzy c-means algorithm

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2016年第3期75卷 909-922页

作者： Shi, Mingyuan chen, Jianping Song, Yang Zhang, Wen Song, Shengyuan Zhang, Xudong Jilin Univ Coll Construct Engn Changchun 130026 Peoples R China Jilin Univ Hosp 1 Dept Dermatol & Venereol Changchun Jilin Peoples R China

Susceptibility analysis is important in any study of debris flows. Unlike debris flows in Southwest china, debris flows in Northern china occur with different characteristics and much lower frequency. However, neglecting the danger of possible debris flows in this northern area may result in a devastating disaster. In this paper, 12 debris flow catchments located near Zhirui town, in Heshigten Banner, Inner Mongolia, china, were investigated. A geographic information system, a global positioning system, and remote sensing were used to determine geological, topographical, morphological, and vegetation factors of influence. Principal component analysis was carried out to convert this set of possibly correlated factors into a set of values of linearly uncorrelated principal components. The accumulative contribution rate of the first five principal components retained most of the information on these factors, accounting for 90.9 %. An improved fuzzy c-means clustering analysis was applied to determine the susceptibility of debris flows in this area. This method is based on a quantum-behaved particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for four of the debris flow catchments were high, six were moderate, and two were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.

关键词： Debris flow susceptibility Principal component analysis Quantum-behaved particle swarm optimization algorithm fuzzy c-means algorithm Debris flow catchments

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