1,722,103 research outputs found
Preparation and Electrochemical Characterization of the Blended Polymer Electrolytes Containing Lithium Salts
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Detecting clusters of different geometrical shapes in microarray gene expression data
No full textMotivation: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. Results: We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data
Effects of sulfidation of Mo nitride and CoMo nitride catalysts on thiophene HDS
No full textMo and CoMo nitrides were prepared by temperature-programmed reduction of the corresponding oxides with flowing ammonia; effects of sulfidation of these catalysts on their thiophene hydrodesulfurization (HDS) activities were investigated. The HDS activity of fresh CoMo nitride is higher than that of Mo2N. The properties of nitrides vary significantly with sulfidation temperature. After sulfiding Mo2N transforms into MoS2 and the CoMo nitride into MoS2 and Co9S8. This indicates that these nitride catalysts are not resistant to sulfur under severe sulfidation conditions. The transformation, on the other hand, results in a synergistic activity of HDS among the three phases present
Fuzzy cluster validation index based on inter-cluster proximity
No full textA new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing the inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index. (C) 2003 Elsevier B.V. All rights reserved.This work was supported by the Korea Science and Engineering Foundation through the Advanced Information Technology Research Center
Stochastic segmentation of severely degraded images using Gibbs random fields
No full textThis paper deals with segmentation of noisy images using Gibbs random field (GRF) with an emphasis on modeling of the region process. For noisy image segmentation using the multi-level logistic (MLL) model with the second-order neighborhood system, which is commonly used in image processing, the segmentation performance is degraded significantly in case of low signal to noise ratio. By comparison with the Ising model that explains the magnetic properties of ferromagnetic material, it is evident that the characteristics of the region process modeled using the MLL model with the second-order neighborhood system are different in nature from the expected characteristics of a region. To solve this problem we added the term of the magnetic energy associated with the magnetic field of a spin system (or image) to the energy function of GRF. Using the modified model for the region process, the result of image segmentation was improved and did not depend on the cooling schedule in simulated annealing
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