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IEEE International Advance Computing Conference

作者： Patel, Rahila Raghuwanshi, M. M. Jaiswal, Anil N. GHRCE CSE M Tech Sem 4 Nagpur Ms India GHRCE Nagpur Ms India NYSS Coll Engn & Res Nagpur Maharashtra India

ISBN: (纸本)9781424429271

Presently, the optimization concept plays an important role in the problems related to engineering management and commerce etc. Recent trends in optimization, points towards the genetic algorithm and evolutionary approaches. Different genetic algorithms are proposed, designed and implemented for the single objective as well as for the multiobjective problems. GAS3[2006](Genetic algorithm with Species and Sexual Selection) proposed by *** and *** is a distributed Quasi steady state real-coded genetic algorithm. In this work, we have modified GAS3 algorithm. We introduce a reclustering module in GAS3 after simple distance based parameter less clustering (species formation). GAS3kM (Modifying Genetic algorithm with Species and Sexual Selection by using k-means algorithm) uses k-means clustering algorithm for reclustering. Experimental results show that GAS3kM has outperformed GAS3 algorithm when tested on unimodal and multimodal test functions.

关键词： Genetic algorithm clustering algorithms optimization evolutionary algorithm k-means clustering algorithm Pattern recognition

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Acceleratedk-means clustering algorithm for colour image quantization

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The Journal of Photographic Science 2008年第1期56卷 29-40页

Thek-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate thek-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of thek-means clustering algorithm without incurring any extra distortion.

关键词： COLOUR IMAGE QUANTIZATION k-means clustering algorithm VECTOR QUANTIZATION LBG algorithm

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Precision of Rough Set clustering

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6th International Conference on Rough Sets and Current Trends in Computing

作者： Lingras, Pawan Chen, Min Miao, Duoqian St Marys Univ Dept Math & Comp Sci Halifax NS B3H 3C3 Canada Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9783540884231

Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.

关键词： Rough sets k-means clustering algorithm precision

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International Symposium on Communication and Information Technologies

作者： Leauhatong, Thurdsak Hamamoto, kazuhiko Atsuta, kiyoaki kondo, Shozo Tokai Univ Grad Sch Sci & Technol Hiratsuka Kanagawa 25912 Japan Tokai Univ Sch Informat & Telecommun engn Hiratsuka Kanagawa 25912 Japan

ISBN: (纸本)9781424423354

This paper proposes a new similarity measure for the content-based image retrieval (CBIR) systems. The similarity measure is based on the multidimensional generalization of the Wald-Wolfowitz (MWW) runs test and the k-means clustering algorithm. The performance comparisons between the proposed method and the current CBIR method based on MWW runs test were performed, and it can be seen that the proposed methods outperform the current method in the sense that the proposed method provides higher performance than the current method for the same computational time.

关键词： Color Histogram Content-Based Image Retrieval k-means clustering algorithm minimal spanning tree Wald-Wolfowitz runs test

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The Research and Realization of clustering algorithm Based On FPGA

The Research and Realization of Clustering Algorithm Based O...

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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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