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Today, fuzzy methods are of common use as they provide tools to handle data sets in a relevant, robust, and interpretable way, making it possible to cope with both imprecision and uncertainties. Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design presents up-to-date techniques for addressing data management problems with logic and memory use. This critical mass of the most sought after findings covers a wide scope of research areas from both a theoretical and experimental point of view.
About the Author
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.



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Author(s) Book :Anne Laurent, Marie-Jeanne Lesot
Publish Year :2009 Publisher : Information Science Reference
You can check by ISBN10/ISBN13 : 1605668583/9781605668581

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