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Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach"

Derval Guillaume; Branders Vincent; Dupont Pierre; Schaus Pierre


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  <identifier identifierType="DOI">10.5281/zenodo.1688740</identifier>
  <creators>
    <creator>
      <creatorName>Derval Guillaume</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6700-3519</nameIdentifier>
      <affiliation>UCLouvain - ICTEAM</affiliation>
    </creator>
    <creator>
      <creatorName>Branders Vincent</creatorName>
      <affiliation>UCLouvain - ICTEAM</affiliation>
    </creator>
    <creator>
      <creatorName>Dupont Pierre</creatorName>
      <affiliation>UCLouvain - ICTEAM</affiliation>
    </creator>
    <creator>
      <creatorName>Schaus Pierre</creatorName>
      <affiliation>UCLouvain - ICTEAM</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach"</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-11-29</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1688740</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1688739</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Synthetic dataset used in &amp;quot;The maximum weighted submatrix coverage problem: A CP approach&amp;quot;.&lt;/p&gt;

&lt;p&gt;Includes both the generated datasets as a zip archive and the python script used to generate them.&lt;/p&gt;

&lt;p&gt;Each instance is composed of two files in the form&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.&lt;/li&gt;
	&lt;li&gt;XxY_K_O_0xN_AxB_Ssolution.txt giving the implanted solution. One submatrix per line. Then two JSON arrays follow, separated by a tabulation. The first is the list of rows selected in the submatrix, the second the columns.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;With:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;X and Y the size of the matrix&lt;/li&gt;
	&lt;li&gt;K the number of submatrices in the implanted solution&lt;/li&gt;
	&lt;li&gt;O the (minimum) overlap percentage of each submatrix&lt;/li&gt;
	&lt;li&gt;N the sigma used for the background noise&lt;/li&gt;
	&lt;li&gt;A and B the size of the implanted submatrices (subject to noise)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
</resource>
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