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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">1705</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201901_17(1).0005</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Decoding of Characteristics of Urban Consumption Based on “Brand Cluster”</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Tang</surname>
            <given-names>Maxim</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Dataway Horizon, Shanghai Bigdata Department, GM</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Wei</surname>
            <given-names>Octavia</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Dataway Horizon, Shanghai Bigdata Department, Senior Consultant</aff>
      </contrib-group>
      <volume>17</volume>
      <issue>1</issue>
      <fpage>107</fpage>
      <lpage>130</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Brand Cluster is proposed based on the background of evolved consumption modes and concepts as well as brand preferences of different categories of consumers. With the support of inter-urban, inter-category and inter-brand big data, after deep learning and profound analysis of consumption relations of different brands, Brand Cluster was born to reflect characteristics of diverse consumers. We try to understand the inner features of 18 clusters of brands and how these clusters look like in different cities, which underlies the practice of city siting of brand owners. Brand Cluster is believed to reveal the relationships between “allies” of brands in a whole new angel of view and in the large. In addition, the make-up of brand clusters in different cities indicate whether a new city is appropriate for brand owners to expand into.</p>
      </abstract>
    </article-meta>
  </front>
</article>
