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 appr...
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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-meansalgorithm) uses k-means clustering algorithm for reclustering. Experimental results show that GAS3kM has outperformed GAS3 algorithm when tested on unimodal and multimodal test functions.
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 p...
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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.
Conventional clusteringalgorithms 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 membershi...
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ISBN:
(纸本)9783540884231
Conventional clusteringalgorithms 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.
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...
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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.
clusteringalgorithm 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 hardwar...
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clusteringalgorithm 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 clusteringalgorithm 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.
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 ...
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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 clusteringalgorithms. 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.
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 reg...
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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.
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 su...
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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.
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-meansalgorithm based on density and ant theory, which resolved the prob...
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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-meansalgorithm 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.
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 ...
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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) clusteringalgorithms. 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.
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