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检索条件"主题词=fuzzy c-means algorithm"

共 259 条记录，以下是211-220 订阅

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Entropy-based Framework Dealing with Error in Software Development Effort Estimation 12

Entropy-based Framework Dealing with Error in Software Devel...

引用

12th International conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： El Koutbi, Salma Idri, Ali Univ Mohammed 5 ENSIAS Software Projects Management Res Team Rabat Morocco

ISBN: (纸本)9789897582509

Software engineering community often investigates the error concerning software development effort estimation as a part, and sometimes, as an improvement of an effort estimation technique. The aim of this paper is to propose an approach dealing with both model and attributes measurement error sources whatever the effort estimation technique used. To do that, we explore the concepts of entropy and fuzzy clustering to propose a new framework to cope with both error sources. The proposed framework has been evaluated with the cOcOMO'81 dataset and the fuzzy Analogy effort estimation technique. The results are promising since the actual confidence interval percentages are closer to those proposed by the framework.

关键词： Error Software Effort Estimation Entropy fuzzy c-means algorithm fuzzy Analogy

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A Novel fuzzy clustering Method with No Outliers Influence

A Novel Fuzzy Clustering Method with No Outliers Influence

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2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)

作者： Kao, Li-Jen Huang, Yo-Ping Hwa Hsia Inst Technol Dept Comp Sci & Informat Engn New Taipei City 23568 Taiwan Natl Taipei Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9783037856512

fuzzy c-means (FcM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FcM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FcM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FcM algorithm can get more accurate centers than the traditional FcM algorithm.

关键词： fuzzy c-means algorithm noise detection outlier detection

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An Accurate algorithm for Identifying Mutually Exclusive Patterns on Multiple Sets of Genomic Mutations 10th

An Accurate Algorithm for Identifying Mutually Exclusive Pat...

引用

10th International Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： He, Siyu Wang, Jiayin Zhao, Zhongmeng Zhang, Xuanping Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9783031349591;9783031349607

In cancer genomics, the mutually exclusive patterns of somatic mutations are important biomarkers that are suggested to be valuable in cancer diagnosis and treatment. However, detecting these patterns of mutation data is an NP-hard problem, which pose a great challenge for computational approaches. Existing approaches either limit themselves to pair-wise mutually exclusive patterns or largely rely on prior knowledge and complicated computational processes. Furthermore, the existing algorithms are often designed for genotype datasets, which may lose the information about tumor clonality, which is emphasized in tumor progression. In this paper, an algorithm for multiple sets with mutually exclusive patterns based on a fuzzy strategy to deal with real-type datasets is proposed. Different from the existing approaches, the algorithm focuses on both similarity within subsets and mutual exclusion among subsets, taking the mutual exclusion degree as the optimization objective rather than a constraint condition. fuzzy clustering of the is done mutations by method of membership degree, and a fuzzy strategy is used to iterate the clustering centers and membership degrees. Finally, the target subsets are obtained, which have the characteristics of high similarity within subsets and the largest number of mutations, and high mutual exclusion among subsets and the largest number of subsets. This paper conducted a series of experiments to verify the performance of the algorithm, including simulation datasets and truthful datasets from TcGA. According to the results, the algorithm shows good performance under different simulation configurations, and some of the mutually exclusive patterns detected from TcGA datasets were supported by published literatures. This paper compared the performance to MEGSA, which is the best and most widely used method at present. The purities and computational efficiencies on simulation datasets outperformed MEGSA.

关键词： Gene mutation Mutual exclusion clustering Membership degree fuzzy c-means algorithm

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Multi-stage FcM-based intensity inhomogeneity correction for MR brain image segmentation

Multi-stage FCM-based intensity inhomogeneity correction for...

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18th International conference on Artificial Neural Networks (IcANN 2008)

作者： Szilagyi, Laszlo Szilagyi, Sandor M. David, Laszlo Benyo, Zoltan Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540875581

Intensity inhomogeneity or intensity non-uniformity (INU) is ail undesired phenomenon that represents the main obstacle for MR, image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This paper proposes a multiple stage fuzzy c-means (FcM) based algorithm for the estimation and compensation of IN U, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, based oil a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

关键词： intensity inhomogeneity fuzzy c-means algorithm image segmentation context dependent filter magnetic resonance imaging morphological operations

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An Improved Method for Disease Prediction using fuzzy Approach 2

An Improved Method for Disease Prediction using Fuzzy Approa...

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2nd IEEE International conference on Advances in computing and communication Engineering (IcAccE)

作者： chetty, Naganna Vaisla, Kunwar Singh Patil, Nagamma Mangalore Inst Tech & Engn Dept CSE Mangalore Karnataka India BTK Inst Tech Dept CSE Dwarahat Uttarakhand India Natl Inst Technol Dept IT Mangalore Karnataka India

ISBN: (纸本)9781479917341

Data Mining has great scope in the field of medicine. In this article we introduced two new fuzzy approaches for prediction of diabetes disease and liver disorder. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for preprocessing and then using KNN for classification. In our first approach fuzzy c-means clustering algorithm is used to cluster the data. Finally, the classification is done using KNN. Similarly, in our second method fuzzy c-means clustering is followed by classification using fuzzy KNN. PIMA diabetes and liver disorder data sets are used to test our methods. We are able to obtain models more precise than any others available in the literature. The second approach produced better result than the first one. classification is done using ten folds cross-validation technique. The introduction of fuzzy algorithms certainly has a positive effect on the outcome of diabetes disease and liver disorder prediction models. These fuzzy models led to remarkable increase in classification accuracy.

关键词： Diabetes disease Liver disorder KNN algorithm fuzzy c-means algorithm fuzzy KNN algorithm

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A study on fuzzy logic theory to predict the process parameters in GMA welding process

A study on fuzzy logic theory to predict the process paramet...

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International conference on Advanced Manufacture

作者： Kim, IS Son, JS Kim, HH Kim, IJ Kang, BY Mokpo Natl Univ Dept Mech Engn Chungnam 534729 South Korea Korea Inst Ind Technol Chungnam 330820 South Korea

ISBN: (纸本)0878499903

Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The fuzzy c-means (FcM) algorithm, which is best known an unsupervised fuzzy clustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzy clustering technique, and the optimal bead geometry can also be achieved.

关键词： fuzzy c-means algorithm GMA welding process parameter bead geometry

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Unsupervised algorithm for radiographic image segmentation based on the Gaussian mixture model

Unsupervised algorithm for radiographic image segmentation b...

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International conference on computer as a Tool (EUROcON 2007)

作者： Mekhalfa, Faiza Nacereddine, Nafaa Goumeidane, Aicha Baya Welding & NDT Res Ctr Image & Signal Proc Lab Cheraga Algeria

ISBN: (纸本)9781424408122

In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.

关键词： weld defect radiographic images image segmentation Gaussian mixture model expectation maximization algorithm fuzzy c-means algorithm

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IMAGE SEGMENTATION algorithm BASED ON cLUSTERING 17

IMAGE SEGMENTATION ALGORITHM BASED ON CLUSTERING

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International conference on Machine Learning and cybernetics (IcMLc)

作者： Wu, Hao Li, Gong-Fa Sun, Ying Tao, Bo Kong, Jian-Yi Xu, Shuang Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430083 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430083 Peoples R China Wuhan Univ Sci & Technol Res Ctr Biol Manipulator & Intelligent Measuremen Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol 3D Printing & Intelligent Mfg Engn Inst Wuhan 430081 Peoples R China

ISBN: (纸本)9781538652145

Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of traditional image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy c-means algorithm and mean-shift algorithm. The fuzzy c-means algorithm does not consider the spatial information of the image. Put forward an fuzzy c-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.

关键词： Image segmentation clustering fuzzy c-means algorithm Mean shift algorithm

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FcM based cF: an Efficient Approach for consolidating Big Data Applications

FCM based CF: an Efficient Approach for Consolidating Big Da...

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International conference on Innovation Information in computing Technologies

作者： Vimali, J. S. Taj, Z. Sabiha Sathyahuma Univ Fac Comp Dept IT Madras Tamil Nadu India

ISBN: (纸本)9781479987887

With regard to rapid development of Internet technology and the increasing volume of data and information, the need for systems that can guide users toward their desired items and services may be felt more than ever. As a result, service-relevant data become too big to be effectively processed by traditional approaches. A naive solution is to decrease the number of services that need to be processed in real time. clustering are such techniques that can reduce the data size by a large factor by grouping similar services together. In this research work, we propose a novel approach called fuzzy c-means clustering-based collaborative Filtering approach (FcM based cF) to overcome the above mentioned issues. Our proposed mechanism consists of two stages: FcM clustering and collaborative filtering. clustering is a preprocessing step to separate the big data into manageable parts. collaborative Filtering is imposed on one of the clusters. The main goal of this research is with respect to users, consolidating and mining their implicit interests from usage reviews or records may be a complement to the explicit interests (ratings). By this means, recommendations can be generated even if there are only few ratings. Finally, our proposed algorithm consolidates the web services and suggests the better web services based on the user navigation.

关键词： Web services big data fuzzy c-means algorithm collaborative Filtering Web mining

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A Novel clustering algorithm Based on Gravity and cluster Merging

A Novel Clustering Algorithm Based on Gravity and Cluster Me...

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6th International conference on Advanced Data Mining and Applications (ADMA)

作者： Zhong, Jiang Liu, Longhai Li, Zhiguo Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9783642173158

fuzzy c-means (FcM) clustering algorithm is commonly used in data mining tasks. It has the advantage of producing good modeling results in many cases. However, it is sensitive to outliers and the initial cluster centers. In addition, it could not get the accurate cluster number during the algorithm. To overcome the above problems, a novel FcM algorithm based on gravity and cluster merging was presented in this paper. By using gravity in this algorithm, the influence of outliers was minimized and the initial cluster centers were selected. And by using cluster merging, an appropriate number of clustering could be specified. The experimental evaluation shows that the modified method can effectively improve the clustering performance.

关键词： fuzzy c-means algorithm Gravity cluster Merge

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