For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic...
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ISBN:
(纸本)9780791845028
For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic, and to be certain a very good clustering solution has been obtained, it may be necessary to run the algorithm thousands of times. To make this feasible in practice, the algorithm must be computationally efficient. Two algorithmic improvements are presented. Together they improve the quality of the results obtained and increase speed significantly for normal clustering problems. The proposed new algorithm is applied to a cordless handheld vacuum cleaner.
This paper presents a new methodology of wear state recognition by using fractal parameters, nuillifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonli...
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This paper presents a new methodology of wear state recognition by using fractal parameters, nuillifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonlinear state point of worn surface is established by fractal dimension, average diagonal length and spectrum width. Further, a steady state sphere is obtained by the nonlinear state point and K-means clustering algorithm. Results show that fractal, multifractal and recurrence parameters characterize the worn surface from different perspectives. They should be used simultaneously to comprehensively characterize the integral structures, partial structures and internal structures of worn surface. The proposed nonlinear state point shows a variation process of concentration-stabilization-separation during the wear process. The wear states can be identified effectively by the relationship between nonlinear state points and steady state sphere.
In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples d...
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ISBN:
(纸本)9781467315067
In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering algorithm. We then apply this algorithm to the real data set with cities temperature interval data. The proposed clustering algorithm actually presents its robustness.
This paper proposes a clustering method, group-driven process in the sociology of the clustering process simulation, whose clustering process simulates the driven process of colony formation in sociology. clustering e...
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ISBN:
(纸本)9781467348836;9781467348812
This paper proposes a clustering method, group-driven process in the sociology of the clustering process simulation, whose clustering process simulates the driven process of colony formation in sociology. clustering experiments show that the clustering accuracy and clustering speed of the algorithm in complex network are superior to the classic optimization Fast-Newman clustering algorithm.
The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural ...
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ISBN:
(纸本)9783037853801
The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural network, a revised clustering method is used to train the hidden node centers of the neural network, in which the subtractive clustering(SC) algorithm was used to determine the initial centers and the fuzzy C - Means(FCM) clustering algorithm to further determined the centers data set. The spread factors and the weights of the neural network are calculated by the modified recursive least squares (MRLS) algorithm for relieving computational burden. The proposed algorithm is verified by its application to the modeling of an electro-hydraulic system, simulation and experiment results clearly indicate the obtained RBF network can model the electro-hydraulic system satisfactorily and comparison results also show that the proposed algorithm performs better than the other methods.
Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-...
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ISBN:
(纸本)9781467311595
Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-based k-means clustering algorithm (SPAKM), has been proposed based on the principle of SPA. The basic ideals and steps of SPAKM are discussed. The proposed algorithm can overcome the limitation of K-means clustering algorithm to certain extent. Finally, cluster analysis experiments of LANDSAT TM image have been made. The results show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of land cover classes of mixed pixels.
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression da...
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Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements;these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.
Recently, in order to improve the energy efficiency of Wireless Sensor Network, wide research has been carried out. At present, most of the presented methods are based on the assumption that nodes can be elected clust...
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ISBN:
(纸本)9780769549354
Recently, in order to improve the energy efficiency of Wireless Sensor Network, wide research has been carried out. At present, most of the presented methods are based on the assumption that nodes can be elected cluster heads with the uniform probability in each round. So it's hard to implement those methods. Based on the theory that node has fixed radio wave radius, this paper present a clustering algorithm based on cluster head reappointment that cluster heads maintain the election right to be cluster heads in several rounds. According to the simulation of the presented protocol, we have proved the energy saving efficiency and the implementation in real wireless sensor networks.
BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large mo...
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BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression *** propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of *** novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.
The principle of kernel methods combined with data description were analyzed and the organic combined model of particle swarm algorithm(PSO) and genetic algorithm(GA) was put forward firstly, then the kernel clusterin...
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ISBN:
(纸本)9783037852828
The principle of kernel methods combined with data description were analyzed and the organic combined model of particle swarm algorithm(PSO) and genetic algorithm(GA) was put forward firstly, then the kernel clustering algorithm under the organic combined model of the GA and the PSO was put forward. Finally, according to the customers' data of certain electric power company, this kernel clustering algorithm was applied to classify the electricity customers into three groups: the first group includes 3 customers, 5 and 7 customers for the second group and the third group respectively. The result shows that the samples can be classified and the center of mass can be obtained using the data description based on kernel methods. But the clustering region is different when the a of kernel method gets the different value. This makes the clustering process to be complex, also spends the longer time. While kernel method's clustering algorithm organically combined with the PSO and GA can get the same result in the shorter time and the calculation is simpler
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