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Multivariate statistics / Data analysis / Cluster analysis / Singular value decomposition / Graph partition / Spectral clustering / Eigenvalues and eigenvectors / Community structure / Modularity / Statistics / Mathematics / Algebra


A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network Motoki Shiga Bioinformatics Center Kyoto University
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Document Date: 2007-06-04 02:24:58


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File Size: 1,09 MB

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City

MEXT / San Jose / /

Company

Computer Sciences / Pearson / John Wiley & Sons / /

Country

Japan / United States / /

Currency

USD / /

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Facility

Kyoto University / Hiroshi Mamitsuka Bioinformatics Center / University Gokasho Uji / Ichigaku Takigawa Bioinformatics Center / University of Texas / /

IndustryTerm

a lot of applications / gene network / data mining / chemical reaction / k-means clustering algorithm / chemical genomics / real metabolic network / given network / k-means algorithm / chemical reactions / chemical compound / high-dimensional data mining / a lot of real-world networks / gene regulatory networks / mining / given weighted network / metabolic networks / metabolic network / normalized network / real scale-free network / real gene network / learning algorithm / random networks / gene networks / /

Organization

Hierarchical / Education and Research Organization for Genome Information Science / Kyoto University / University of Texas / /

Person

Nat / Hiroshi Mamitsuka / Motoki Shiga / /

Position

von Mises-Fisher / cluster representative / ABSTRACT General / representative / von Mises-Fisher distribution mixture / /

Product

Pentax K-x Digital Camera / /

ProvinceOrState

Texas / California / /

Technology

genomics / chemical genomics / Bioinformatics / genetic engineering / hybridization / clustering algorithm / metabolic networks / learning algorithm / k-means algorithm / proposed algorithm / DNA Chip / gene expression / k-means clustering algorithm / Data mining / 2.4 Proposed Algorithm / /

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