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<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Mining Evolving data Streams for Frequent Patterns</title>
<creator>Laur, Pierre-Alain</creator>
<creator>Nock, Richard</creator>
<creator>Symphor, Jean-Émile</creator>
<creator>Poncelet, Pascal</creator>
<contributor>Groupe de Recherche en Informatique et Mathématiques Appliquées Antilles-Guyane (GRIMAAG) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Fouille de données environnementales (TATOO) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>ISSN: 0031-3203</source>
<source>Pattern Recognition</source>
<publisher>Elsevier</publisher>
<identifier>lirmm-00365474</identifier>
<identifier>https://hal-lirmm.ccsd.cnrs.fr/lirmm-00365474</identifier>
<source>https://hal-lirmm.ccsd.cnrs.fr/lirmm-00365474</source>
<source>Pattern Recognition, Elsevier, 2007, 40, pp.492-503</source>
<language>en</language>
<subject>[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>A data stream is a potentially uninterrupted flow of data. Mining this flow makes it necessary to cope with uncertainty, as only a part of the stream can be stored. In this paper, we evaluate a statistical technique which biases the estimation of the support of patterns, so as to maximize either the precision or the recall, as chosen by the user, and limit the degradation of the other criterion. Theoretical results show that the technique is not far from the optimum, from the statistical standpoint. Experiments performed tend to demonstrate its potential, as it remains robust even under significant distribution drifts.</description>
<date>2007</date>
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