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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">090209</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201104_09(2).0009</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Identifying Groups: A Comparison of Methodologies</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Eshghi</surname>
            <given-names>Abdolreza</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Bentley University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Haughton</surname>
            <given-names>Dominique</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">1Bentley University, 2Universit´e of Toulouse I</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Legrand</surname>
            <given-names>Pascal</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">Groupe ESC Clermont-CRCGM</aff>
      </contrib-group>
      <volume>9</volume>
      <issue>2</issue>
      <fpage>271</fpage>
      <lpage>291</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: This paper describes and compares three clustering techniques: traditional clustering methods, Kohonen maps and latent class models. The paper also proposes some novel measures of the quality of a clustering. To the best of our knowledge, this is the first contribution in the literature to compare these three techniques in a context where the classes are not known in advance.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Cluster analysis</kwd>
        <kwd>Kohonen maps</kwd>
        <kwd>latent class analysis</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
