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检索条件"主题词=Fuzzy C-means Algorithm"

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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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Applications of Partition based clustering algorithms: A Survey

Applications of Partition based Clustering Algorithms: A Sur...

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IEEE International conference on computational Intelligence and computing Research (IccIc)

作者： Dharmarajan, A. Velmurugan, T. Bharathiar Univ Ctr Res & Dev Coimbatore 046 Tamil Nadu India DG Vaishnav Coll Res Dept Comp Sci Madras 106 Tamil Nadu India

ISBN: (纸本)9781479915941;9781479915958

Data mining is one of the interesting research areas in database technology. In data mining, a cluster is a set of data objects that are similar to one another with in a cluster and are different to the entities in the former clusters. clustering is the efficient method in data mining in order to process huge data sets. The core methodology of clustering is used in many domains like academic result analysis of institutions. Also, the methods are very well suited in machine learning, clustering in medical dataset, pattern recognition, image mining, information retrieval and bioinformatics. The clustering algorithms are categorized based upon different research phenomenon. Varieties of algorithms have recently occurred and were effectively applied to real-life data mining problems. This survey mainly focuses on partition based clustering algorithms namely k-means, k-Medoids and fuzzy c-means In particular, they applied mostly in medical data sets. The importance of the survey is to explore the various applications in different domains.

关键词： clustering algorithm fuzzy c-means algorithm k-means algorithm k-Medoids algorithm

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fuzzy clustering optimized with genetic algorithms: Application for hybrid speech recognition system 4

Fuzzy clustering optimized with genetic algorithms: Applicat...

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4th International conference on control, Decision and Information Technologies (coDIT)

作者： Lazli, Lilia Boukadoum, Mounir Mohamed, Otmane Ait Univ Quebec ETS Dept Elect Engn Montreal PQ Canada Univ Quebec UQAM Dept Comp Sci Montreal PQ Canada Concordia Univ Dept Elect Engn & Comp Sci Montreal PQ Canada

ISBN: (纸本)9781509064656

In this paper, we report experimental results of hybrid system using Hidden Markov Models/Multi-Layer Perceptron (HMM/MLP) model as acoustic model and based on the fuzzy c-means (FcM) clustering with optimization with Genetic algorithm (GA). In this context, we use the result of FcM clustering as initial population of GA, this allows training the GA with a population of empirically generated chromosomes and not randomly initialized. Our results on speech recognition tasks show an increase in the estimates of the posterior probabilities of the correct words after training. We demonstrate the effectiveness of the proposed clustering approach in large-vocabulary speaker-independent continuous speech recognition with regard to the three baseline systems : Discrete HMM, hybrid HMM/MLP with K-means and FcM clustering.

关键词： Speech clustering fuzzy c-means algorithm Genetic algorithms Hybrid HMM/MLP recognition system

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Abnormal Wind Power Data Identification Based on the Improved FcM algorithm and considering the Influence of Wind Speed 9

Abnormal Wind Power Data Identification Based on the Improve...

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9th International conference on Power and Energy Systems (IcPES)

作者： Fang, chen Zhang, Zhiyu Su, Xiangjing Zeng, Ping Tian, Shuxin State Grid Shanghai Elect Power Res Inst Grid Technol Ctr Shanghai Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728126586

In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzy c-means (FcM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-china Sea offshore wind power ScADA database.

关键词： abnormal data identification fuzzy c-means algorithm wind power and data correction

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

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

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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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Detecting Temporal changes in customer Behavior

Detecting Temporal Changes in Customer Behavior

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International Electrical Engineering congress (iEEcON)

作者： Bose, Indranil chen, Xi Indian Inst Management Calcutta Kolkata India Zhejiang Univ Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781479931743

Extant research has studied customer behavior in a static manner. But customer clustering can be used to identify the dynamic behavioral patterns of customers over a period of time. We develop a method for extending the standard fuzzy c-means clustering algorithm for detection of temporal changes in customer data. The study using real-life data leads to detection of appearance of new clusters and disappearance of old clusters. Using cluster validity indexes the novel method is shown to lead to formation of clusters that are better than those produced by the fuzzy c-means (FcM) algorithm.

关键词： clusters fuzzy c-means algorithm Revenue Temporal data Usage Validity index

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An efficient fault diagnosis strategy based on SVDD and fuzzy clustering for ground-based electronic equipment

An efficient fault diagnosis strategy based on SVDD and fuzz...

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IEEE International conference on Signal Processing, communications and computing (IcSPcc)

作者： Wang, cheng Yang, Huahui Meng, chen Army Engn Univ Measurement Engn Dept Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781728117089

In this paper, an efficient fault detection approach which employs the Support Vector Data Description (SVDD) and fuzzy c-means algorithm (FcM) is proposed for ground-based electronic equipment. Firstly, the FcM method is applied to fault pattern mining in which the prior knowledge of equipment faults is difficult to be known. Then SVDD model is trained with different faults data independently for multi-classification. This fault diagnosis strategy can be used in health condition monitoring for ground-based electronic equipment. The experimental results verify its effectiveness in fault diagnosis with high accuracy and real-time performance.

关键词： Fault diagnosis support vector domain description fuzzy c-means algorithm condition monitoring electronic equipment

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A Novel Approach for color Image Segmentation Using Iterative Partitioning Mean Shift clustering algorithm

A Novel Approach for Color Image Segmentation Using Iterativ...

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2015 International conference on communications and Signal Processing (IccSP)

作者： Naik, P. Pedda Sadhu Gopal, T. Venu JNTUK CSE Dept Kakinada India JNTUHCE CSE Dept Jagityal India

ISBN: (纸本)9781479980819

Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.

关键词： Image segmentation H&E stain images fuzzy c-means algorithm mean shift clustering

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