Laurent A., Lesot M.-J. (Eds.) Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design

Information Science Reference, 2009. — 467 p. — (Premier Reference Source). — ISBN: 1605668583, 9781605668581Today, 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.Introductory Chapters.
Electronic Hardware for Fuzzy Computation.
Scaling Fuzzy Models.
Databases and Queries.
Using Fuzzy Song Sets in Music Warehouses.
Mining Association Rules from Fuzzy DataCubes.
Scalable Reasoning with Tractable Fuzzy Ontology Languages.
A Random Set and Prototype Theory Model of Linguistic Query Evaluation.
A Flexible Language for Exploring Clustered Search Results.
Summarization.
Linguistic Data Summarization: A High Scalability through the Use of Natural Language?
Human Focused Summarizing Statistics Using OWA Operators.
(Approximate) Frequent Item Set Mining Made Simple with a Split and Merge Algorithm.
Fuzzy Association Rules to Summarise Multiple Taxonomies in Large Databases.
Fuzzy Cluster Analysis of Larger Data Sets.
Fuzzy Clustering with Repulsive Prototypes.
Real-World Challenges.
Early Warning from Car Warranty Data using a Fuzzy Logic Technique.
High Scale Fuzzy Video Mining.
Fuzzy Clustering of Large Relational Bioinformatics Datasets.