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    SDM
    2009
    SIAM
    225views Data Mining» more  SDM 2009»
    Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
    16 years 3 months ago
    Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
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    Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) ...
    Fei Wang, Chris H. Q. Ding, Tao Li
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    SDM
    2009
    SIAM
    193views Data Mining» more  SDM 2009»
    Agglomerative Mean-Shift Clustering via Query Set Compression.
    16 years 3 months ago
    Agglomerative Mean-Shift Clustering via Query Set Compression.
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    Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets...
    Xiaotong Yuan, Bao-Gang Hu, Ran He
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    SDM
    2009
    SIAM
    105views Data Mining» more  SDM 2009»
    Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
    16 years 3 months ago
    Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
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    The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
    Gerhard Paaß, Frank Reichartz
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    SDM
    2009
    SIAM
    140views Data Mining» more  SDM 2009»
    Straightforward Feature Selection for Scalable Latent Semantic Indexing.
    16 years 3 months ago
    Straightforward Feature Selection for Scalable Latent Semantic Indexing.
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    Latent Semantic Indexing (LSI) has been validated to be effective on many small scale text collections. However, little evidence has shown its effectiveness on unsampled large sca...
    Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen
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    SDM
    2009
    SIAM
    167views Data Mining» more  SDM 2009»
    Detecting Communities in Social Networks Using Max-Min Modularity.
    16 years 3 months ago
    Detecting Communities in Social Networks Using Max-Min Modularity.
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    Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common ...
    Jiyang Chen, Osmar R. Zaïane, Randy Goebel
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