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IEEE International Conference on Computational Cybernetics

作者： Taher Ben Arab Mourad Zribi Afif Masmoudi Laboratoire d'Informatique Signal et Image de la Cote d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Laboratoire d'Informatique Signal et Image de la C?te d'Opale (LISIC-EA 4491) ULCO 50 Rue Ferdinand Buisson BP 719 62228 Calais Cedex France. Laboratoire de Probabilités et Statistique Faculté des Sciences de Sfax Université de Sfax. B. P. 1171 Sfax Tunisie.

ISBN: (纸本)9781479900626

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a k-means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a k-means clustering algorithm and Expectation-Maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta type-II attributes a specific distribution type according to different parameters. Different adapted algorithms (namely k-means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II Expectation Maximization algorithm k-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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Establishment and Implementation of Securities Company Customer Classification Model Based on clustering Analysis and PCA

Establishment and Implementation of Securities Company Custo...

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International Conference on Control Engineering and Communication Technology (ICCECT)

作者： Liu Bin Qiu Huayong Shen Yizhen Chinese Acad Sci Grad Univ Ind Secur Stock Ltd Co Fuzhou Peoples R China Ind Secur Stock Ltd Co Fuzhou Peoples R China

ISBN: (纸本)9781467344999;9780769548814

In the paper, advanced data mining technology is applied to the analysis of customers' historical exchange data in the customer classification model of securities exchange. The accuracy and effectiveness of the results is remarkably improved. The process involves using data warehouse to achieve the storage of massive customer transaction data, the construction of the fundamental indicator system, the selection of the indicators using PCA and the construction of the customer classification model using k-means clustering algorithm. The application of these technologies significantly improves the accuracy and effectiveness the customer classification indicators, enabling the results closer to the mettle of the customers and basically solving the key points and difficulties in the suitability management.

关键词： securities data mining k-means clustering algorithm principal component analysis PCA customer classification model

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An Ontology Learning Method Based on Document clustering

An Ontology Learning Method Based on Document Clustering

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Wei, Xianmin Weifang Univ Comp & Commun Engn Sch Weifang Peoples R China

ISBN: (纸本)9783037852828

Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

关键词： ontology learning document clustering k-means clustering algorithm similarity ontology merging

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A Novel Method for Text and Non-Text Segmentation in Document Images

A Novel Method for Text and Non-Text Segmentation in Documen...

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International Conference on Communication and Signal Processing

作者： S. Deivalakshmi P. Palanisamy Gayatri Vishwanathan the Electronics and Communication Engineering Department National Institute of Technology Tiruchirappalli TN 620015 India

ISBN: (纸本)9781467348652

Segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of Wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and k-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam Document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete Wavelet Transform Document Image Segmentation GLCM Features k-means clustering algorithm

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Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

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Physics Procedia 2012年 25卷 911-916页

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Shanghai China Railway &Urban Mass Transit Research Institute Tongji University Shanghai China

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks’ hidden layer computed with k-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network k-means clustering algorithm Multi-step prediction

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Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Prediction Study on Anti-Slide Control of Railway Vehicle Ba...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Railway &Urban Mass Transit Research Institute Tongji University

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with k-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network k-means clustering algorithm Multi-step prediction

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An extended k-means technique for clustering moving objects

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EGYPTIAN INFORMATICS JOURNAL 2011年第1期12卷 45-51页

作者： Ossama, Omnia Mokhtar, Hoda M. O. El-Sharkawi, Mohamed E. Cairo Univ Fac Comp & Informat Cairo Egypt

k-means algorithm is one of the basic clustering techniques that is used in many data mining applications. In this paper we present a novel pattern based clustering algorithm that extends the k-means algorithm for clustering moving object trajectory data. The proposed algorithm uses a key feature of moving object trajectories namely, its direction as a heuristic to determine the different number of clusters for the k-means algorithm. In addition, we use the silhouette coefficient as a measure for the quality of our proposed approach. Finally, we present experimental results on both real and synthetic data that show the performance and accuracy of our proposed technique. (C) 2011 Faculty of Computers and Information, Cairo University. Production and hosting by Elsevier B. V. All rights reserved.

关键词： clustering moving objects Moving objects databases Mining moving object trajectories k-means clustering algorithm

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Portable measurement system for the objective evaluation of the spasticity of hemiplegic patients based on the tonic stretch reflex threshold

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MEDICAL ENGINEERING & PHYSICS 2011年第1期33卷 62-69页

作者： kim, keo Sik Seo, Jeong Hwan Song, Chul Gyu Chonbuk Natl Univ Sch Elect & Informat Engn Ctr Adv Image & Informat Technol Jeonju 561756 Jeonbuk South Korea Chonbuk Natl Univ Dept Rehabil Med Sch Med Jeonju 561712 Jeonbuk South Korea

The clinical scales used for the evaluation of the spasticity have some drawbacks, in spite of their simplicity and ease of assessment, and their inter- and intra-rater reliability remains controversial. The aim of this study is to develop a portable system for the objective and reliable evaluation of the spasticity based on the k-means clustering of the tonic stretch reflex threshold (TSRT) and to compare the discrimination performance of the level of spasticity determined by our method with that by the conventional modified Ashworth scale (MAS). Fifteen hemiplegic patients (7 males and 8 females, age: 63.5 +/- 15.6) participated in the study. The average and standard deviation values of the TSRTs were 127.9 +/- 1.6, 121.8 +/- 1.5 and 117.9 +/- 1.3 in groups G1, G2 and G3, respectively, and there were significant differences between the TSRTs of each group (p<0.05). Also, our groups classified by the criteria of the TSRT had a strong negative correlation (r=-0.95, r(2) = 0.90, p<0.05) between the level of spasticity and TSRTs. These results demonstrated that our system could be clinically more useful for the quantitative and reliable discrimination of the spasticity than the conventional MAS. Crown Copyright (C) 2010 Published by Elsevier Ltd on behalf of IPEM. All rights reserved.

关键词： k-means clustering algorithm Portable system Spasticity Tonic stretch reflex threshold

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Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

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kNOWLEDGE-BASED SYSTEMS 2011年第3期24卷 378-385页

作者： Shen, Wei Guo, Xiaopen Wu, Chao Wu, Desheng Univ Toronto RiskLab Toronto ON M5S 3G3 Canada N China Elect Power Univ Sch Business & Adm Beijing 102206 Peoples R China Univ Waterloo Waterloo ON N2L 3G1 Canada Reykjavik Univ Menntavegur 1 IS-101 Reykjavik Iceland

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a k-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA. BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6 + MA5 + ASY4 was the optimum group with the least errors. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial fish swarm algorithm Radial basis function neural network k-means clustering algorithm Data mining Shanghai Stock Exchange Index

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Research on the Text clustering algorithm based on Latent Semantic Analysis and Optimization

Research on the Text Clustering Algorithm based on Latent Se...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Wang Chun-hong Nan Li-Li Ren Yao-Peng Computer Science and Technology ,Yun cheng University, Yun Cheng,044000

The text clustering based on Vector Space Model has problems, such as high-dimensional and sparse, unable to solve synonym and polyseme etc. And meanwhile, k-means clustering algorithm has shortcomings, which depends on the initial clustering center and needs to fix the number of clusters in advance. Aiming at these problems, in this paper, a text clustering algorithm based on Latent Semantic Analysis and Optimization is proposed. This algorithm can not only overcome the problems of Vector Space Model, but also can avoid the shortcomings of k-means algorithm. And compared with the text clustering algorithm based on Latent Semantic Analysis and the text clustering algorithm based on Vector Space Model and optimization, our algorithm is proved which can preferably improve the effect of text clustering, and upgrade the precision ratio and recall ration of text.

关键词： text clustering Vector Space Model Latent Semantic Analysis clustering optimization k-means clustering algorithm

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