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Showing 1 to 2 of 2 for “"Data Clustering and Visualization"”.

  1. Data Clustering And Visualization Through Matrix Factorization

    <p>Clustering is traditionally an unsupervised task which is to find natural groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information</p> <p>from unlabeled data.</p> <p>In order to present the …

    wayne-thes Repository record for Data Clustering And Visualization Through Matrix Factorization (opens in a new tab)

  2. Learning Statistical and Geometric Models from Microarray Gene Expression Data

    In this dissertation, we propose and develop innovative data modeling and analysis methods for extracting meaningful and specific information about disease mechanisms from microarray gene expression data. To provide a high-level overview of gene expression data for easy and insightful understanding …

    vt Repository record for Learning Statistical and Geometric Models from Microarray Gene Expression Data (opens in a new tab)