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.
Marie-Jeanne Lesot obtained her PhD from the University Pierre and Marie Curie in 2005 and since 2006 she is an associate professor in the department of Computer Science of Paris 6 (LIP6) and member of the Machine Learning and Information Retrieval (MALIRE) department. Her research interests include fuzzy machine learning, in particular fuzzy clustering, typicality and fuzzy prototypes, and similarity measures. [Editor]