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SMILE: A novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables.

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Title: SMILE: A novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables.
Authors: Emily, Mathieu1,2 mathieu.emily@agrocampus-ouest.fr, Hitte, Christophe3, Mom, Alain2,4
Source: Computational Statistics & Data Analysis. Jul2016, Vol. 99, p171-188. 18p.
Subjects: Spanning trees, Sparse approximations, Cluster analysis (Statistics), Contingency tables, Similarity (Geometry)
Abstract: A novel statistical procedure for clustering individuals characterized by sparse-specific profiles is introduced in the context of data summarized in sparse contingency tables. The proposed procedure relies on a single-linkage clustering based on a new dissimilarity measure designed to give equal influence to sparsity and specificity of profiles. Theoretical properties of the new dissimilarity are derived by characterizing single-linkage clustering using Minimum Spanning Trees. Such characterization allows the description of situations for which the proposed dissimilarity outperforms competing dissimilarities. Simulation examples are performed to demonstrate the strength of the new dissimilarity compared to 11 other methods. The analysis of a genomic dataset dedicated to the study of molecular signatures of selection is used to illustrate the efficiency of the proposed method in a real situation. [ABSTRACT FROM AUTHOR]
Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Periodical
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  Data: <searchLink fieldCode="JN" term="%22Computational+Statistics+%26+Data+Analysis%22">Computational Statistics & Data Analysis</searchLink>. Jul2016, Vol. 99, p171-188. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Spanning+trees%22">Spanning trees</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+approximations%22">Sparse approximations</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Contingency+tables%22">Contingency tables</searchLink><br /><searchLink fieldCode="DE" term="%22Similarity+%28Geometry%29%22">Similarity (Geometry)</searchLink>
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  Data: A novel statistical procedure for clustering individuals characterized by sparse-specific profiles is introduced in the context of data summarized in sparse contingency tables. The proposed procedure relies on a single-linkage clustering based on a new dissimilarity measure designed to give equal influence to sparsity and specificity of profiles. Theoretical properties of the new dissimilarity are derived by characterizing single-linkage clustering using Minimum Spanning Trees. Such characterization allows the description of situations for which the proposed dissimilarity outperforms competing dissimilarities. Simulation examples are performed to demonstrate the strength of the new dissimilarity compared to 11 other methods. The analysis of a genomic dataset dedicated to the study of molecular signatures of selection is used to illustrate the efficiency of the proposed method in a real situation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.csda.2016.01.017
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 171
    Subjects:
      – SubjectFull: Spanning trees
        Type: general
      – SubjectFull: Sparse approximations
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
        Type: general
      – SubjectFull: Contingency tables
        Type: general
      – SubjectFull: Similarity (Geometry)
        Type: general
    Titles:
      – TitleFull: SMILE: A novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables.
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            NameFull: Emily, Mathieu
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            NameFull: Hitte, Christophe
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            NameFull: Mom, Alain
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            – D: 01
              M: 07
              Text: Jul2016
              Type: published
              Y: 2016
          Identifiers:
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              Value: 01679473
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            – Type: volume
              Value: 99
          Titles:
            – TitleFull: Computational Statistics & Data Analysis
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