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Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design

Anne Laurent 1 Marie-Jeanne Lesot 2
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Today, fuzzy methods provide tools to handle data sets in relevant, robust and interpretable ways, making it possible to model and exploit imprecision and uncertainty in data modeling and data mining. Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design presents innovative, cutting-edge fuzzy techniques that highlight the relevance of fuzziness for huge data sets in the perspective of scalability issues, from both a theoretical and experimental point of view. It covers a wide scope of research areas including data representation, structuring and querying as well as information retrieval and data mining. It encompasses different forms of databases, including data warehouses, data cubes, tabular or relational data, and many applications among which music warehouses, video mining, bioinformatics, semantic web and data streams.
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Anne Laurent, Marie-Jeanne Lesot. Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design. Anne Laurent; Marie-Jeanne Lesot. Information Science Reference, 380 p., 2009, 978-1-60566-858-1. ⟨lirmm-00408734⟩

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