<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-01614989</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-21T02:43:24+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">A Novel Locally Multiple Kernel k-means Based on Similarity</title>
            <author role="aut">
              <persName>
                <forename type="first">Shuyan</forename>
                <surname>Fan</surname>
              </persName>
              <idno type="halauthorid">1249067-0</idno>
              <affiliation ref="#struct-356390"/>
              <affiliation ref="#struct-239582"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Shifei</forename>
                <surname>Ding</surname>
              </persName>
              <email type="md5">2161c78ea9b9a4e44a6f311f2a5860e4</email>
              <email type="domain">cumt.edu.cn</email>
              <idno type="idhal" notation="numeric">990783</idno>
              <idno type="halauthorid" notation="string">1070031-990783</idno>
              <affiliation ref="#struct-356390"/>
              <affiliation ref="#struct-239582"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Mingjing</forename>
                <surname>Du</surname>
              </persName>
              <idno type="halauthorid">1249068-0</idno>
              <affiliation ref="#struct-356390"/>
              <affiliation ref="#struct-239582"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Xiao</forename>
                <surname>Xu</surname>
              </persName>
              <idno type="idhal" notation="numeric">771451</idno>
              <idno type="halauthorid" notation="string">824488-771451</idno>
              <idno type="IDREF">https://www.idref.fr/187516375</idno>
              <affiliation ref="#struct-356390"/>
              <affiliation ref="#struct-239582"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Hal</forename>
                <surname>Ifip</surname>
              </persName>
              <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
              <email type="domain">inria.fr</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2017-10-11 16:57:44</date>
              <date type="whenModified">2024-03-26 16:24:04</date>
              <date type="whenReleased">2017-10-11 17:00:32</date>
              <date type="whenProduced">2016-11-18</date>
              <date type="whenEndEmbargoed">2019-01-01</date>
              <ref type="file" target="https://inria.hal.science/hal-01614989v1/document">
                <date notBefore="2019-01-01"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://inria.hal.science/hal-01614989v1/file/433802_1_En_3_Chapter.pdf" id="file-1614989-1657081">
                <date notBefore="2019-01-01"/>
              </ref>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="200187">
                <persName>
                  <forename>Hal</forename>
                  <surname>Ifip</surname>
                </persName>
                <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
                <email type="domain">inria.fr</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-01614989</idno>
            <idno type="halUri">https://inria.hal.science/hal-01614989</idno>
            <idno type="halBibtex">fan:hal-01614989</idno>
            <idno type="halRefHtml">&lt;i&gt;9th International Conference on Intelligent Information Processing (IIP)&lt;/i&gt;, Nov 2016, Melbourne, VIC, Australia. pp.22-30, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-48390-0_3"&gt;&amp;#x27E8;10.1007/978-3-319-48390-0_3&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.22-30, &amp;#x27E8;10.1007/978-3-319-48390-0_3&amp;#x27E9;</idno>
            <availability status="restricted">
              <licence target="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 - Attribution<ref corresp="#file-1614989-1657081"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="IFIP">IFIP - International Federation for Information Processing</idno>
            <idno type="stamp" n="IFIP-AICT" corresp="IFIP">IFIP Advances in Information and Communication Technology</idno>
            <idno type="stamp" n="IFIP-TC" corresp="IFIP">IFIP Technical Committees </idno>
            <idno type="stamp" n="IFIP-TC12" corresp="IFIP-TC">TC12 - Artificial Intelligence</idno>
            <idno type="stamp" n="IFIP-IIP" corresp="IFIP-AICT">IFIP-IIP</idno>
            <idno type="stamp" n="IFIP-AICT-486" corresp="IFIP-AICT">Intelligent Information Processing VIII</idno>
          </seriesStmt>
          <notesStmt>
            <note type="commentary">Part 1: Machine Learning</note>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">A Novel Locally Multiple Kernel k-means Based on Similarity</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Shuyan</forename>
                    <surname>Fan</surname>
                  </persName>
                  <idno type="halauthorid">1249067-0</idno>
                  <affiliation ref="#struct-356390"/>
                  <affiliation ref="#struct-239582"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Shifei</forename>
                    <surname>Ding</surname>
                  </persName>
                  <email type="md5">2161c78ea9b9a4e44a6f311f2a5860e4</email>
                  <email type="domain">cumt.edu.cn</email>
                  <idno type="idhal" notation="numeric">990783</idno>
                  <idno type="halauthorid" notation="string">1070031-990783</idno>
                  <affiliation ref="#struct-356390"/>
                  <affiliation ref="#struct-239582"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Mingjing</forename>
                    <surname>Du</surname>
                  </persName>
                  <idno type="halauthorid">1249068-0</idno>
                  <affiliation ref="#struct-356390"/>
                  <affiliation ref="#struct-239582"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Xiao</forename>
                    <surname>Xu</surname>
                  </persName>
                  <idno type="idhal" notation="numeric">771451</idno>
                  <idno type="halauthorid" notation="string">824488-771451</idno>
                  <idno type="IDREF">https://www.idref.fr/187516375</idno>
                  <affiliation ref="#struct-356390"/>
                  <affiliation ref="#struct-239582"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">IFIP Advances in Information and Communication Technology</title>
                <meeting>
                  <title>9th International Conference on Intelligent Information Processing (IIP)</title>
                  <date type="start">2016-11-18</date>
                  <date type="end">2016-11-21</date>
                  <settlement>Melbourne, VIC</settlement>
                  <country key="AU">Australia</country>
                </meeting>
                <imprint>
                  <biblScope unit="serie">Intelligent Information Processing VIII</biblScope>
                  <biblScope unit="volume">AICT-486</biblScope>
                  <biblScope unit="pp">22-30</biblScope>
                  <date type="datePub">2016</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1007/978-3-319-48390-0_3</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Clustering analysis</term>
                <term xml:lang="en">Similarity measure</term>
                <term xml:lang="en">Kernel k-means</term>
                <term xml:lang="en">Multiple kernel clustering</term>
              </keywords>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Most of multiple kernel clustering algorithms aim to find the optimal kernel combination and have to calculate kernel weights iteratively. For the kernel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel multiple kernel k-means algorithm is proposed based on similarity measure. Our similarity measure meets the requirements of the clustering hypothesis, which can describe the relations between data points more reasonably by taking local and global structures into consideration. We assign to each data point a local scale parameter and combine the parameter with density factor to construct kernel matrix. According to the local distribution, the local scale parameter of Gaussian kernel is generated adaptively. The density factor is inspired by density-based algorithm. However, different from density-based algorithm, we first find neighbor data points using k nearest neighbor method and then find density-connected sets by union-find set method. Experiments show that the proposed algorithm can effectively deal with the clustering problem of datasets with complex structure or multiple scales.</p>
            </abstract>
            <particDesc>
              <org type="consortium">TC 12</org>
            </particDesc>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="institution" xml:id="struct-356390" status="VALID">
          <orgName>China University of Mining and Technology</orgName>
          <orgName type="acronym">CUMT</orgName>
          <date type="start">2016-09-14</date>
          <desc>
            <address>
              <addrLine>丁11 Xueyuan Rd, Haidian, Beijing, Chine</addrLine>
              <country key="CN"/>
            </address>
          </desc>
        </org>
        <org type="institution" xml:id="struct-239582" status="VALID">
          <idno type="ROR">https://ror.org/034t30j35</idno>
          <orgName>Chinese Academy of Sciences [Changchun Branch]</orgName>
          <orgName type="acronym">CAS</orgName>
          <desc>
            <address>
              <addrLine>52 Sanlihe Rd. - Xicheng District - Beijing, China 100864</addrLine>
              <country key="CN"/>
            </address>
            <ref type="url">http://english.cas.cn/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>