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combining supervised and unsupervised learning for data clustering

NEURAL cOMPUTING & APPLIcATIONS 2006年第3-4期15卷 289-297页

作者： corsini, Paolo Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron Informat Tele I-56122 Pisa Italy

clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approach, however, is not viable when dissimilarity is conceptual rather than metric. In this paper, we propose to extract the dissimilarity relation directly from the available data. To this aim, we train a feedforward neural network with some pairs of points with known dissimilarity. Then, we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzy relational clustering algorithm. An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used (possibly partially supervised) clustering algorithms based on spatial dissimilarity.

关键词： fuzzy relational clustering algorithms fuzzy c-means algorithm dissimilarity relations feedforward neural networks cluster shape

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The NURBS human body modeling using local knot removal

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FIBERS AND POLYMERS 2005年第4期6卷 348-354页

作者： Jo, JW Han, SS Yeungnam Univ Sch Text Gyongsan 712749 South Korea

These days consumers' various demands are accelerating research on apparel manufacturing system including automatic measurement, pattern generation, and clothing simulation. Accordingly, methods of reconstructing human body from point-clouds measured using a three dimensional scanning device are required for apparel cAD system to support these functions. In particular, we present in this study a human body reconstruction method focused on two issues, which are the decision of the number of control point for each sectional curve with error bound and the local knot removal for reducing the unusual concentration of control points. The approximation of sectional curves with error bounds as an approximation criterion leads all sectional curves to their own particular shapes apart from the number of control points. In addition, the application of the local knot removal to construction of human body sectional curves reduces the unusual concentration of control points effectively. The results may be used to produce an apparel cAD system as an automatic pattern generation system and a clothing simulation system through the low level control of NUBS or NURBS.

关键词： human body modeling NURBS fuzzy c-means algorithm surface approximation

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Research on storage and encryption method for private electronic communication information based on fuzzy rules

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INTERNATIONAL JOURNAL OF cOMPUTER APPLIcATIONS IN TEcHNOLOGY 2024年第4期74卷 342-350页

作者： Sun, Rui Wang, Zhenzhen Liu, Huanhuan Ji, Yu Hebei Inst Mech & Elect Technol Dept Informat Engn Shijiazhuang Hebei Peoples R China Hebei Inst Mech & Elect Technol Org Dept Shijiazhuang Hebei Peoples R China

To improve the security of private electronic communication information, the paper proposes an information storage encryption method based on fuzzy rules. Firstly, after establishing a private electronic communication information set, fill in the missing values. Secondly, using the fuzzy c-means algorithm, electronic communication information is classified according to fuzzy rules and stored in the underlying information database according to the index of communication information. Finally, for the data in the information database, the AES algorithm is applied to encrypt lightweight information, and a hybrid algorithm of DES and Ecc is applied to encrypt high-dimensional sparse information. The experimental results show that when the number of plaintext bits in private electronic communication information increases from 30 to 150 bits, the number of ciphertext bits in this method increases from 65 to 309 bits, indicating that this method can effectively ensure the security of private electronic communication information.

关键词： private electronic communication information information classification fuzzy c-means algorithm fuzzy rules information encryption

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Selection of Minimum Rules from a fuzzy TSK Model Using a PSO-FcM combination

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JOURNAL OF cONTROL AUTOMATION AND ELEcTRIcAL SYSTEMS 2023年第2期34卷 384-393页

作者： Brikh, Lamine Guenounou, Ouahib Bakir, Toufik Univ Bejaia Fac Technol Lab Technol Ind & Informat LTII Bejaia 06000 Algeria Univ Bourgogne Lab ImViA EA 7535 Dijon France

This work proposes an efficient and promising method for linear and nonlinear dynamic systems modeling. Unlike previous methods, we combine fuzzy c-means (FcM) and modified Particle Swarm Optimization (PSO) algorithms. The FcM allows clustering large nonlinear data sets (good rule-base parameters initialization). The PSO algorithm is used to optimize the parameters of the fuzzy system rules (initialized by FcM). This combination allows reducing the rule-base of TSK fuzzy model considering the three goals: have a good initialization of the parameters to be optimized, handling complex systems with ensuring a high accuracy and a low complex algorithm structure. Indeed, from a given fuzzy rule-base, FcM-PSO selects a subset of important fuzzy rules based on the threshold criterion of each rule. The interaction between the particles improves iteratively the quality of each fuzzy rule. Simulation results using two well-known benchmark functions show the efficiency of the proposed approach when compared to previous works.

关键词： Evolutionary algorithm fuzzy c-means algorithm Optimization Particle swarm optimization TSK fuzzy logic

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computer Aided Diagnosis Based Hand Thermal Image Analysis: A Potential Tool for the Evaluation of Rheumatoid Arthritis

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JOURNAL OF MEDIcAL AND BIOLOGIcAL ENGINEERING 2018年第4期38卷 666-677页

作者： Umapathy, Snekhalatha Vasu, Sowmiya Gupta, Nilkantha SRM Univ Dept Biomed Engn Madras 603203 Tamil Nadu India SRM Univ Ctr Environm Nucl Res Madras 603203 Tamil Nadu India

The aim of the study was to evaluate the potential of the thermogram in diagnosing rheumatoid arthritis and to compare the implementation of k-means algorithm and fuzzy c means algorithm using a computer aided diagnostic tool for classification of rheumatoid arthritis (RA) and normal based on the feature extracted from the segmented thermal image. The skin surface temperature measurement, thermal image segmentation based on k-means and fuzzy c-means algorithm and the feature extraction were used in this study. The average skin surface temperature measured at the second meta carpo-phalangeal (McP) and McP3 in RA patients (35.40 +/- 0.6 A degrees c and 35.52 +/- 0.7 A degrees c, respectively) were significantly higher (p < 0.01) than those measured in normal subjects (33.66 +/- 0.2 A degrees c and 33.74 +/- 0.2 A degrees c, respectively). The mean difference in temperature between RA patients and healthy controls in the McP2 and McP3 region was found to be 1.74 and 1.78 A degrees c respectively. The receiver operating characteristics (ROc) curve depicted a sensitivity of 86.6% and specificity of 79% achieved in the McP region of the hand thermal image. Thermal image segmentation using the k-means algorithm provided better segmentation results compared to the fuzzy c-means algorithm in diagnosing the disease. Therefore, the computer aided diagnostic based thermography method could be used as a validated quantification method for interpreting and evaluating arthritis.

关键词： Rheumatoid arthritis K-means algorithm fuzzy c-means algorithm Feature extraction Receiver operating characteristics (ROc) curve

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Analysis of integrated energy-load characteristics based on sparse clustering and compressed sensingInspec keywordsOther keywords

IET ENERGY SYSTEMS INTEGRATION

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IET ENERGY SYSTEMS INTEGRATION 2019年第3期1卷 194-201页

作者： Wang, He Hou, Yongshan Yu, Huanan Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China

An integrated energy system not only provides a platform for multi-energy coupling utilisation but also satisfies users' diversified energy demands. However, in view of the enormous amount of integrated energy data and the difficulty of analysing the characteristics of that data, an integrated energy-analysis method based on sparse clustering and compressed sensing is proposed in this study. This method uses the fuzzy c-means algorithm to construct an over-complete dictionary and then compresses, collects, and reconstructs the integrated energy data using the compressed sensing theory method. This process analyses integrated energy-load characteristics accurately and also solves the problem of low data-transmission efficiency. Simulation results show that the method is suitable for analysing and processing integrated load data in integrated energy systems.

关键词： compressed sensing pattern clustering buildings (structures) energy consumption power engineering computing load forecasting fuzzy set theory integrated energy-load characteristics sparse clustering multienergy coupling utilisation integrated energy data compressed sensing theory method fuzzy c-means algorithm over-complete dictionary

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classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

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cOGNITIVE NEURODYNAMIcS 2013年第6期7卷 477-494页

作者： Zhang, Jian-Hua Peng, Xiao-Di Liu, Hua Raisch, Joerg Wang, Ru-Bin E China Univ Sci & Technol Dept Automat Shanghai 200237 Peoples R China E China Univ Sci & Technol Inst Cognit Neurodynam Shanghai 200237 Peoples R China Tech Univ Berlin Control Syst Grp D-10587 Berlin Germany Max Planck Inst Dynam Complex Tech Syst Syst & Control Theory Grp D-39106 Magdeburg Germany

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FcM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FcM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

关键词： Operator functional state fuzzy c-means algorithm Psychophysiological measures Feature extraction Pattern classification

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Forecasting tourist arrivals using dual decomposition strategy and an improved fuzzy time series method

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NEURAL cOMPUTING & APPLIcATIONS 2023年第10期35卷 7161-7183页

作者： Liang, Xiaozhen Wu, Zhikun Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Tourist arrivals forecasting has become an increasingly hot issue due to its important role in the tourism industry and hence the whole economy of a country. However, owing to the complex characteristics of tourist arrivals series, such as seasonality, randomness, and non-linearity, forecasting tourist arrivals remains a challenging task. In this paper, a hybrid model of dual decomposition and an improved fuzzy time series method is proposed for tourist arrivals forecasting. In the novel model, two stages are mainly involved, i.e., dual decomposition and integrated forecasting. In the first stage, a dual decomposition strategy, which can overcome the potential defects of individual decomposition approaches, is designed to fully extract the main features of the tourist arrivals series and reduce the data complexity. In the second stage, a fuzzy time series method with fuzzy c-means algorithm as the discretization method is developed for prediction. In the empirical study, the proposed model is implemented to predict the monthly tourist arrivals to Hong Kong from USA, UK, and Germany. The results show that our hybrid model can obtain more accurate and more robust prediction results than benchmark models. Relative to the benchmark fuzzy time series models, the hybrid models using traditional decomposition methods and strategies, as well as the traditional single prediction models, our proposed model shows a significant improvement, with the improvement percentages at about 80, 70, and 50%, respectively. Therefore, we can conclude that the proposed model is a very promising tool for forecasting future tourist arrivals or other related fields with complex time series.

关键词： Forecast of tourist arrivals Dual decomposition strategy fuzzy c-means algorithm fuzzy time series

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fuzzy c-means clustering-based Multilayer Perceptron Neural Network for Liver cT Images Automatic Segmentation

Fuzzy C-means Clustering-based Multilayer Perceptron Neural ...

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2010 chinese control and Decision conference

作者： Yuqian Zhao~(1,2),Yunlong Zan~1,Xiaofang Wang~1,Guiyuan Li~2 1.School of Info-Physics & Geomatics Engineering,central South University,changsha 410083,china 2.cancer Research Institute,central South University,changsha 410078,china

A new liver segmentation algorithm is ***,the threshold method was used to remove the ribs and spines in the initial image,and the fuzzy c-means clustering algorithm and morphological reconstruction filtering were used to segment the initial liver cT *** the multilayer perceptron neural network was trained by the segmentation result of initial image with the back-propagation *** adjacent slice cT image was segmented with the trained multilayer perceptron neural ***,morphological reconstruction filtering was used to smooth the contour of the liver *** experimental results show that the proposed algorithm can effectively segment the livers from cT images,despite the gray level similarity of adjacent organs and different gray level of tumors in the liver.

关键词： Liver Segmentation fuzzy c-means algorithm Multilayer Perceptron Neural Network Morphological Reconstruction Filtering

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Image change Detection Based on fuzzy clustering and Neural Networks

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INTERNATIONAL JOURNAL OF ADVANcED cOMPUTER ScIENcE AND APPLIcATIONS 2024年第6期15卷 495-503页

作者： Wang, chenwei Li, Xiating Jiangxi V&T Coll Commun Sch Informat Engn Nanchang 330013 Peoples R China

In the change detection of synthetic aperture radar images, the image quality and change detection accuracy are difficult to meet the application requirements due to the influence of speckle noise. Therefore, the study improved the fuzzy c-means algorithm by introducing fuzzy membership degree and Gabor texture features. Features were weighted through channel attention, resulting in an image change detection model, namely, the fuzzy local information c-means for Gabor textures and multi-scale channel attention wavelet convolutional neural network. The segmentation accuracy of the model was 0.995, which improved by 0.119 compared to the traditional fuzzy c-means algorithm. When adding multiplicative noise with different variances, the noise variance reached 0.30, and the accuracy of the algorithm still reached 0.982. In practical application analysis, the detection and segmentation accuracy of river images was 0.983 with a partition coefficient of 0.935, and the segmentation accuracy of farmland images was 0.960 with a partition coefficient of 0.902. Therefore, the algorithm has good stability and anti-noise performance. The algorithm can be widely applied in various fields of synthetic aperture radar image change detection, such as disaster assessment, urban development monitoring, and environmental change monitoring. This paper provides more accurate analysis results, which help with policy formulation and effective resource management.

关键词： fuzzy c-means algorithm fuzzy membership degree Gabor texture channel attention neural networks synthetic aperture radar images

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