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2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会

作者： Jun Feng Wenbo Xu Zhilei Chai College of Information,Jiangnan University,Wuxi,214122,China

clustering algorithm is an important method of researching neural *** paper introduces a method to implement this algorithm,under the FPGA, implement k-means clustering algorithm,accelerating the arithmetic by hardware,more rapidness,more *** result shows that implement this algorithm take advantage of hardware better than software.k-means clustering algorithm is a kind of clustering algorithm which can be dealt with FPGA, because it is a parallel algorithm,it will be completely get faster by FPGA than it used to *** that is all the reason why we implement k-means clustering algorithm by FPGA.

关键词： FPGA Neural Network k-means clustering algorithm

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Structure pattern analysis using term rewriting and clustering algorithm

Structure pattern analysis using term rewriting and clusteri...

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作者： Fu, Xuezheng Georgia State University

学位级别：Ph.D.

Biological data is accumulated at a fast pace. However, raw data are generally difficult to understand and not useful unless we unlock the information hidden in the data. knowledge/information can be extracted as the patterns or features buried within the data. Thus data mining, aims at uncovering underlying rules, relationships, and patterns in data, has emerged as one of the most exciting fields in computational science. In this dissertation, we develop efficient approaches to the structure pattern analysis of RNA and protein three dimensional structures. The major techniques used in this work include term rewriting and clustering algorithms. Firstly, a new approach is designed to study the interaction of RNA secondary structures motifs using the concept of term rewriting. Secondly, an improved k-means clustering algorithm is proposed to estimate the number of clusters in data. A new distance descriptor is introduced for the appropriate representation of three dimensional structure segments of RNA and protein three dimensional structures. The experimental results show the improvements in the determination of the number of clusters in data, evaluation of RNA structure similarity, RNA structure database search, and better understanding of the protein sequence-structure correspondence. keywords. Data mining, knowledge discovery, Term rewriting, k-means clustering algorithm, Validation measure, Stability, and Bioinformatics.

关键词： Data mining knowledge discovery Term rewriting k-means clustering algorithm Validation measure Stability Bioinformatics

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Two texture segmentation of document image using wavelet packet analysis

Two texture segmentation of document image using wavelet pac...

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9th International Conference on Advanced Communication Technology (ICACT 2007)

作者： Lee, Geum-Boon Odoyo, Wilfred O. Lee, Jae-Hoon Chung, Il-Yong Cho, Beom-Joon Chosun Univ Dept Comp Engn Kwangju South Korea

ISBN: (纸本)9788955191318

In this paper, we present a text segmentation method using wavelet packet analysis and k-means clustering algorithm. This approach assumes that the text and non-text regions are considered as two different texture regions. The text segmentation is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multiscale features, we compute the local energy and intensify the features before adapting the k-means clustering algorithm based on the unsupervised learning rule. The results show that our text segmentation method is effective for document images scanned from newspapers and journals.

关键词： wavelet packet analysis document image segmentation k-means clustering algorithm energy estimation

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An Analysis of Government Subsidies Enterprise Training Based On CDC algorithm

An Analysis of Government Subsidies Enterprise Training Base...

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The Sixth International Conference on Information and Management Sciences

作者： Menz-Ru Huang Ruey-Gwo Chung Tung-Shou Chen Hsuan-Yi Cheng Yung-Ching Lin Department of Industrial Education and Technology National Changhua University of Education 500 Taiwan China Department of Industrial Education and Technology National Changhua University of Education 500 Taiwan China Graduate School of Computer Science and Information Technology National Taichung Institute of Technology Graduate School of Computer Science and Information Technology National Taichung Institute of Technology Graduate School of Computer Science and Information Technology National Taichung Institute of Technology

Taiwan government subsidizes more than 10 billion funds to enterprises every year for on-the-job training The main goal is to guide enterprise to provide on-the-job training for employees after obtains the suitable subsidy fund,and improves quality of work ***,after various enterprises receive the subsidy,whether can really provide effective training which fulfills the market demand is worth to *** this research using data mining method to analysis database from Bureau of Employment Vocational Training(BEVT)in Taiwan base on Combined Density-and Constraint-based algorithm (CDC).Since CDC algorithm has the characteristic which can reduce the effect of noise distortion and outliers,it can be more accurate and faster to analysis the data in order to find the relationship between the amount of government subsidy and the effectiveness of on-the-job *** algorithm basically has two *** division stage,it uses the concept of data density to clustering the data,and isolates the noise distortion and outliers simultaneously in order to discover the cluster centroid,then take the k-mean cluster center as the concept,take larger cluster as the center, merges into smaller clusters with limited mean error,finally completes the calculation *** finding shows significant positive correlation between the amount of government subsidy and the return on investment of training. Since enterprise has links training investment to organizational performance,therefore,it will also receive more government *** if the enterprise continue conduct structure training program,the enterprise will have more complete quality training environment to continuously re-invest in and upgrade the competence of their human resources.

关键词： CDC on-the-job Training k-means clustering algorithm

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The k-means clustering algorithm based on density and ant colony

The K-means clustering algorithm based on density and ant co...

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International Conference on Neural Networks and Signal Processing

作者： Peng, YQ Hou, XD Liu, S Hebei Univ & Technol Sch Comp Sci & Engn Tianjin 300130 Peoples R China

ISBN: (纸本)0780377028

The ant algorithm is a new evolutional method, k-means and the density-cluster are familiar cluster analysis. In this paper, we proposed a new k-means algorithm based on density and ant theory, which resolved the problem of local minimal by the random of ants and handled the initial parameter sensitivity of k-means. In addition it combined idea of density and made the ants searching selectable. With the experiments it was proved that the algorithm we proposed improved the efficiency and precision of cluster.

关键词： data mining pattern clustering sensitivity ant colony density k-means clustering algorithm density-cluster cluster analysis parameter sensitivity

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A prototype classification method and its use in a hybrid solution for multiclass pattern recognition

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PATTERN RECOGNITION 2006年第4期39卷 624-634页

作者： Chou, CH Lin, CC Liu, YH Chang, F Acad Sinica Inst Informat Sci Taipei 115 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

In this paper, we propose a prototype classification method that employs a learning process to determine both the number and the location of prototypes. This learning process decides whether to stop adding prototypes according to a certain termination condition, and also adjusts the location of prototypes using either the k-means (kM) or the fuzzy c-means (FCM) clustering algorithms. When the prototype classification rnethod is applied, the support vector machine (SVM) method can be used to post-process the top-rank candidates obtained during the prototype learning or matching process. We apply this hybrid solution to handwriting recognition and address the convergence behavior and runtime consumption of the prototype construction process, and discuss how to combine our prototype classifier with SVM classifiers to form an effective hybrid classifier. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： fuzzy c-means clustering algorithm handwritten character recognition hybrid classifier k-means clustering algorithm prototype learning support vector machine

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k-means adapted algorithm based on spatial contiguity relations

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Jisuanji Gongcheng/Computer Engineering 2006年第21期32卷 50-51+75页

作者： Wang, Haiqi Wang, Jinfeng National Key Lab. of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research Chinese Acad. of Sciences. Beijing 100101 China Dongying 257061 China Graduate School Chinese Acad. of Sciences Beijing 100039 China

Spatial object has not only non-spatial attribute properties but also spatial properties related with space coordinates and topological structures. When using the traditional clustering methods to classify spatial objects, the objects of the same class may appear in non-adjacent spatial positions because spatial relationships are not been considered. The k-means adapts algorithm based on spatial contiguity relations regards spatial contiguities of the neighboring objects as a restrained condition. So the clustering result not only reflects the similarities of attributes but also reflects spatial adjacent relations, and furthermore reviews spatial distribution patterns of different classes. Therefore, this adapted algorithm is more suitable for the clustering analysis of spatial objects than the traditional k-means method.

关键词： Contiguity matrix Contiguity relation k-means clustering algorithm Spatial object

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Fault detection based on RBF neural network in a hydraulic position servo system

Fault detection based on RBF neural network in a hydraulic p...

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6th World Congress on Intelligent Control and Automation

作者： Liu, Hongmei Ouyang, Pingechao Wang, Shaoping Beijing Univ Aeronaut & Astronaut FAc 303 Beijing 100083 Peoples R China

ISBN: (纸本)1424403316

Due to the inherent nonlinearity existed in the hydraulic system, the failure mechanism become complex and the failure characteristics are difficult to extract. Model-based fault diagnosis method depends heavily on the accuracy of mathematical model. An accuracy mathematical model of the process, however, is difficult to avail because of the nonlinearity and ripple coupling in actual hydraulic servo system. Therefore, Robustness of fault diagnosis method based on approximate linear model is worse. A failure observer based on RBF neural network is developed to realize failure detection. The Gaussian function is used for hidden node function, whose centers are adjusted by improved k-means clustering algorithm presented. The weights of output layer are obtained based on improved LS (least square) presented. The trained RBF observer, working concurrently with the,, actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, then rebuilds the system states. The output of the system is accurately estimated. By comparing the estimated output with the actual measurements, residual signal is generated and then analyzed to report the occurrence of faults. The experimental results demonstrate that the failure observer based on RBF neural network is effective in detecting the failure of the hydraulic position servo system.

关键词： status estimate failure detection RBF neural network hydraulic position servo system k-means clustering algorithm

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The new k-windows algorithm for improving the k-means clustering algorithm

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JOURNAL OF COMPLEXITY 2002年第1期18卷 375-391页

作者： Vrahatis, MN Boutsinas, B Alevizos, P Pavlides, G Univ Patras UPAIRC Dept Math GR-26500 Patras Greece UOP UPAIRC Dept Business Adm GR-26500 Patras Greece UOP UPAIRC Dept Comp Engn & Inf GR-26500 Patras Greece

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of the best for implementing the clustering process. The k-means has a time complexity that is dominated by the product of the number of patterns, the number of clusters, and the number of iterations. Also, it often converges to a local minimum. In this paper, we present an improvement of the k-means clustering algorithm, aiming at a better time complexity and partitioning accuracy. Our approach reduces the number of patterns that need to be examined for similarity, in each iteration, using a windowing technique. The latter is based on well known spatial data structures, namely the range tree, that allows fast range searches. (C) 2002 Elsevier Science (USA).

关键词： k-means clustering algorithm unsupervised teaming data mining range search

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Discriminant image resolution: a novel multivariate image analysis method utilizing a spatial classification constraint in addition to bilinear nonnegativity

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1-2期77卷 18-31页

作者： Hancewicz, TM Wang, JH Unilever Res & Dev Edgewater NJ 07020 USA JHW Consulting Cardinal ON K0E 1E0 Canada

Spectroscopic image analysis can be characterized as two different and distinct problems depending on the kind of information required from the solution. In its simplest form, the data can be decomposed into two submatrices, each of which carries different aspects of pure component information. Typically, this means information about pure spectra and pure intensities are obtained from the solution. This is the well-known and well-characterized bilinear form that by itself cannot guarantee a unique solution due to the rotational ambiguity inherent in the mathematical solution. This problem has been addressed in a number of ways by different authors using novel constraints applied to the least-squares solution. This usually takes the form of natural constraints as suggested by Tauler as the standard methodology to improve the resolution of data during alternative least squares (ALS) iterative process. A second type of multivariate image analysis problem is proposed in this paper that is quite different from the tradition methods and in some ways potentially is more useful. This involves the solution as a class problem in which the relevant information is not necessarily contained in pure component information, but rather, in unique combinations of the pure components that are allowed to be spatially collocated. This discriminant image resolution (DIR) method theoretically can be treated as a more generalized solution to the problem because the distribution of components is allowed to freely mix in simplified combinations of solutions. The result is a constrained least-squares solution where the constraints are more limited and therefore less restrictive. The constraints in this case employ the results of probabilistic class partition information by applying Bayesian discriminant clustering to the intensity submatrix. This amounts to a spatial constraint because the probability of class association is used as a way of limiting the components that are allowed to appear

关键词： discriminant image resolution multivariate curve resolution multivariate image analysis k-means clustering algorithm Fuzzy c-means clustering algorithm Bayes classifier modified alternating least squares regression

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