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Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation

The Journal of China Universities of Posts and Telecommunications 2021年第1期28卷 24-40页

作者： Wu Chengmao Cao Zhuo School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121China

To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means(FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.

关键词： fuzzy clustering image segmentation entropy-like divergence robust clustering algorithm

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A robust Fuzzy c-Means clustering algorithm for Incomplete Data

A Robust Fuzzy c-Means Clustering Algorithm for Incomplete D...

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International Conference on Life System Modeling and Simulation (LSMS) / International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE)

作者： Li, Jinhua Song, Shiji Zhang, Yuli Li, Kang Tsinghua Univ TNList Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland

ISBN: (纸本)9789811063732;9789811063725

Date sets with missing feature values are prevalent in clustering analysis. Most existing clustering methods for incomplete data rely on imputations of missing feature values. However, accurate imputations are usually hard to obtain especially for small-size or highly corrupted data sets. To address this issue, this paper proposes a robust fuzzy c-means (RFCM) clustering algorithm, which does not require imputations. The proposed RFCM represents the missing feature values by intervals, which can be easily constructed using the K-nearest neighbors method, and adopts a min-max optimization model to reduce the impact of noises on clustering performance. We give an equivalent tractable reformulation of the min-max optimization problem and propose an efficient solution method based on smoothing and gradient projection techniques. Experiments on UCI data sets validate the effectiveness of the proposed RFCM algorithm by comparison with existing clustering methods for incomplete data.

关键词： robust FCM Interval data robust clustering algorithm

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robust Bayesian partition for extended target Gaussian inverse Wishart PHD filter

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IET SIGNAL PROCESSING 2014年第4期8卷 330-338页

作者： Zhang, Yongquan Ji, Hongbing Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Extended target Gaussian inverse Wishart PHD filter is a promising filter. However, when the two or more different sized extended targets are spatially close, the simulation results conducted by Granstrom et al. show that the cardinality estimate is much smaller than the true value for the separating tracks. In this study, the present authors call this phenomenon as the cardinality underestimation problem, which can be solved via a novel robust clustering algorithm, called Bayesian partition, derived by combining the fuzzy adaptive resonance theory with Bayesian theorem. In Bayesian partition, alternative partitions of the measurement set are generated by the different vigilance parameters. Simulation results show that the proposed partitioning method has better tracking performance than that presented by Granstrom et al., implying good application prospects.

关键词： adaptive resonance theory Bayes methods filtering theory fuzzy set theory pattern clustering target tracking robust Bayesian partition extended target Gaussian inverse Wishart PHD filter cardinality estimate cardinality underestimation problem robust clustering algorithm fuzzy adaptive resonance theory Bayesian theorem partitioning method

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A robust clustering procedure for fuzzy data

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2010年第1期60卷 151-165页

作者： Hung, Wen-Liang Yang, Miin-Shen Lee, E. Stanley Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan Natl Hsinchu Univ Educ Grad Inst Comp Sci Hsinchu Taiwan Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA

In this paper we propose a robust clustering method for handling LR-type fuzzy numbers. The proposed method based on similarity measures is not necessary to specify the cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering method, especially robust to outliers, different cluster shapes and initial guess. We then apply this algorithm to three real data sets. These are Taiwanese tea, student data and patient blood pressure data sets. Because tea evaluation comes under an expert subjective judgment for Taiwanese tea, the quality levels are ambiguity and imprecision inherent to human perception. Thus. LR-type fuzzy numbers are used to describe these quality levels. The proposed robust clustering method successfully establishes a performance evaluation system to help consumers better understand and choose Taiwanese tea. Similarly. LR-type fuzzy numbers are also used to describe data types for student and patient blood pressure data. The proposed method actually presents good clustering results for these real data sets. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： robust clustering algorithm LR-type fuzzy number Outlier robustness Similarity measure Tea evaluation

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Applying robust directional similarity based clustering approach RDSC to classification of gene expression data

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Journal of Bioinformatics and Computational Biology 2006年第3期4卷 745-768页

作者： Li, H.X. Wang, Shitong Xiu, Yu School of Information Engineering Southern Yangtze University Wuxi China Department of MEEM City University of Hong Kong Hong Kong Hong Kong

Despite the fact that the classification of gene expression data from a cDNA microarrays has been extensively studied, nowadays a robust clustering method, which can estimate an appropriate number of clusters and be insensitive to its initialization has not yet been developed. In this work, a novel robust clustering approach, RDSC, based on the new Directional Similarity measure is presented. This new approach RDSC, which integrates the Directional Similarity based clustering algorithm, DSC, with the Agglomerative Hierarchical clustering algorithm, AHC, exhibits its robustness to initialization and its capability to determine the appropriate number of clusters reasonably. RDSC has been successfully employed to both artificial and benchmarking gene expression datasets. Our experimental results demonstrate its distinctive superiority over the conventional method Kmeans and the two typical directional clustering algorithms SPKmeans and moVMF. © 2006 Imperial College Press.

关键词： clustering validity Directional similarity measure Gene expression datasets robust clustering algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2004年第4期26卷 434-448页

作者： Yang, MS Wu, KL Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan Kun Shan Univ Technol Dept Informat Management Tainan 71023 Taiwan

This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach to clustering on the basis of a total similarity objective function related to the approximate density shape estimation. We show that the data points in SCM can self-organize local optimal cluster number and volumes without using cluster validity functions or a variance-covariance matrix. The proposed clustering method is also robust to noise and outliers based on the influence function and gross error sensitivity analysis. Therefore, SCM exhibits three robust clustering characteristics: 1) robust to the initialization (cluster number and initial guesses), 2) robust to cluster volumes (ability to detect different volumes of clusters), and 3) robust to noise and outliers. Several numerical data sets and actual data are used in the SCIVI to show these good aspects. The computational complexity of SCM is also analyzed. Some experimental results of comparing the proposed SCM with the existing methods show the superiority of the SCM method.

关键词： robust clustering algorithm fuzzy clustering alternating optimization algorithm total similarity noise

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robust growing neural gas algorithm with application in cluster analysis

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NEURAL NETWORKS 2004年第8-9期17卷 1135-1148页

作者： Qin, AK Suganthan, PN Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG) framework, called robust Growing Neural Gas (RGNG) network.(1) By incorporating several robust strategies, such as outlier resistant scheme, adaptive modulation of learning rates and cluster repulsion method into the traditional GNG framework, the proposed RGNG network possesses better robustness properties. The RGNG is insensitive to initialization, input sequence ordering and the presence of outliers. Furthermore, the RGNG network can automatically determine the optimal number of clusters by seeking the extreme value of the Minimum Description Length (MDL) measure during network growing process. The resulting center positions of the optimal number of clusters represented by prototype vectors are close to the actual ones irrespective of the existence of outliers. Topology relationships among these prototypes can also be established. Experimental results have shown the superior performance of our proposed method over the original GNG incorporating MDL method, called GNG-M. in static data clustering tasks on both artificial and UCI data sets. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： robust clustering algorithm robust growing neural gas prototypes outlier resistant minimum description length topology formation

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robust growing neural gas algorithm with application in cluster analysis

Robust growing neural gas algorithm with application in clus...

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Workshop on New Developments in Self-Organizing Systems

作者： Qin, AK Suganthan, PN Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

关键词： robust clustering algorithm robust growing neural gas prototypes outlier resistant minimum description length topology formation

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