New Frontiers in Mining Complex Patterns electronic resource Third International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014, Nancy, France, September 19, 2014, Revised Selected Papers / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras.
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TextSeries: Lecture Notes in Computer SciencePublication details: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XII, 211 p. 61 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319178769Subject(s): Computer Science | Database management | Data mining | Information Storage and Retrieval | Artificial intelligence | Computer Science | Data Mining and Knowledge Discovery | Database Management | Information Storage and Retrieval | Artificial Intelligence (incl. Robotics)DDC classification: 006.312 LOC classification: QA76.9.D343Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2014, held in conjunction with ECML-PKDD 2014 in Nancy, France, in September 2014. The 13 revised full papers presented were carefully reviewed and selected from numerous submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: classification and regression; clustering; data streams and sequences; applications.
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2014, held in conjunction with ECML-PKDD 2014 in Nancy, France, in September 2014. The 13 revised full papers presented were carefully reviewed and selected from numerous submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: classification and regression; clustering; data streams and sequences; applications.
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