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    » Projective ART for clustering data sets in high dimensional ...
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    WIRN
    2005
    Springer
    225views Artificial Intelligence» more  WIRN 2005»
    Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
    14 years 25 days ago
    Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
    Download  homes.dsi.unimi.it
    We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
    Alberto Bertoni, Giorgio Valentini
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    SDM
    2004
    SIAM
    253views Data Mining» more  SDM 2004»
    Density-Connected Subspace Clustering for High-Dimensional Data
    13 years 8 months ago
    Density-Connected Subspace Clustering for High-Dimensional Data
    Download  www.dbs.informatik.uni-muenchen.de
    Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective ...
    Peer Kröger, Hans-Peter Kriegel, Karin Kailin...
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    BMEI
    2008
    IEEE
    222views Biomedical Imaging» more  BMEI 2008»
    Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps
    13 years 9 months ago
    Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps
    Download  math.georgiasouthern.edu
    The importance of gene expression data in cancer diagnosis and treatment by now has been widely recognized by cancer researchers in recent years. However, one of the major challen...
    Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wun...
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    PAMI
    2006
    141views more  PAMI 2006»
    Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameter
    13 years 7 months ago
    Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameter
    Download  www.cs.cmu.edu
    We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...
    Stéphane Lafon, Ann B. Lee
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    SDM
    2007
    SIAM
    133views Data Mining» more  SDM 2007»
    On Point Sampling Versus Space Sampling for Dimensionality Reduction
    13 years 8 months ago
    On Point Sampling Versus Space Sampling for Dimensionality Reduction
    Download  www.charuaggarwal.net
    In recent years, random projection has been used as a valuable tool for performing dimensionality reduction of high dimensional data. Starting with the seminal work of Johnson and...
    Charu C. Aggarwal
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