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<h1 class="page-header">Correlating Time-Related Data Sources with Co-clustering</h1>
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<span property="dc:title" content="Correlating Time-Related Data Sources with Co-clustering" class="rdf-meta element-hidden"></span><span property="sioc:num_replies" content="0" datatype="xsd:integer" class="rdf-meta element-hidden"></span> </header>
<div id="biblio-node"><span class="Z3988" title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Correlating+Time-Related+Data+Sources+with+Co-clustering&rft.series=Lecture+Notes+in+Computer+Science&rft.isbn=978-3-540-85480-7&rft.date=2008&rft.volume=5175&rft.spage=264&rft.epage=279&rft.aulast=Koutsonikola&rft.aufirst=Vassiliki&rft.au=Petridou%2C+Sophia&rft.au=Vakali%2C+Athena&rft.au=Hacid%2C+Hakim&rft.au=Benatallah%2C+Boualem&rft.pub=Springer"></span><div class="table-responsive">
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<tr><td class="biblio-row-title">Title</td><td>Correlating Time-Related Data Sources with Co-clustering</td> </tr>
<tr><td class="biblio-row-title">Publication Type</td><td>Conference Paper</td> </tr>
<tr><td class="biblio-row-title">Year of Publication</td><td>2008</td> </tr>
<tr><td class="biblio-row-title">Authors</td><td><a href="publications%3Ff[author]=96.html" rel="nofollow">Koutsonikola, Vassiliki A.</a>, <a href="publications%3Ff[author]=230.html" rel="nofollow">Sophia G. Petridou</a>, <a href="publications%3Ff[author]=5.html" rel="nofollow">Athena Vakali</a>, <a href="publications%3Ff[author]=194.html" rel="nofollow">Hakim Hacid</a>, and <a href="publications%3Ff[author]=36.html" rel="nofollow">Boualem Benatallah</a></td> </tr>
<tr><td class="biblio-row-title">Editor</td><td><a href="publications%3Ff[author]=246.html" rel="nofollow">Bailey, James</a>, <a href="publications%3Ff[author]=247.html" rel="nofollow">David Maier</a>, <a href="publications%3Ff[author]=248.html" rel="nofollow">Klaus-Dieter Schewe</a>, <a href="publications%3Ff[author]=249.html" rel="nofollow">Bernhard Thalheim</a>, and <a href="publications%3Ff[author]=250.html" rel="nofollow">Xiaoyang Sean Wang</a></td> </tr>
<tr><td class="biblio-row-title">Book Title</td><td>WISE</td> </tr>
<tr><td class="biblio-row-title">Publisher</td><td>Springer</td> </tr>
<tr><td class="biblio-row-title">ISBN Number</td><td>978-3-540-85480-7</td> </tr>
<tr><td class="biblio-row-title">Abstract</td><td><p>A huge amount of data is circulated and collected every dayon a regular time basis. Given a pair of such datasets, it might be possibleto reveal hidden dependencies between them since the presence of the onedataset elements may influence the elements of the other dataset and viceversa. Furthermore, the impact of these relations may last during a periodinstead of the time point of their co-occurrence. Mining such relationsunder those assumptions is a challenging problem. In this paper, we studytwo time-related datasets whose elements are bilaterally affected overtime. We employ a co-clustering approach to identify groups of similarelements on the basis of two distinct criteria: the direction and durationof their impact. The proposed approach is evaluated using time-relatednews and stock’s market real datasets.</p>
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