Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Books Year : 2009

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

Anne Laurent
Marie-Jeanne Lesot

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.
Fichier principal
Vignette du fichier
lirmm-00408734v1-EditorProof.pdf (44.68 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

lirmm-00408734 , version 1 (09-10-2019)

Identifiers

  • HAL Id : lirmm-00408734 , version 1

Cite

Anne Laurent, Marie-Jeanne Lesot (Dir.). 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⟩
172 View
10 Download

Share

Gmail Facebook X LinkedIn More