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

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.

Anne Laurent has been an assistant professor at the LIRMM lab since September 2003. As a member of the TATOO group, she works on data mining, OLAP Mining, sequential pattern mining, tree mining, stream mining both for trends and exceptions detections and is particularly interested in the study of the use of fuzzy logic to provide more valuable results, while remaining scalable. Anne Laurent has numerous collaborations with companies, including small and big businesses. She serves as reviewer in the main conferences and journals related to data mining and fuzzy logic.

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 and member of the Machine Learning and Information Retrieval department. Her research interests include fuzzy machine learning, in particular fuzzy clustering, typicality and fuzzy prototypes, and similarity measures.