1.

Record Nr.

UNINA9910828884603321

Autore

Maji Pradipta <1976->

Titolo

Rough-fuzzy pattern recognition : applications in bioinformatics and medical imaging / / Pradipta Maji, Sankar K. Pal

Pubbl/distr/stampa

Hoboken, NJ, : Wiley, 2012

ISBN

1-283-42501-7

9786613425010

1-118-11971-1

1-118-11972-X

1-118-11969-X

Edizione

[1st ed.]

Descrizione fisica

1 online resource (313 p.)

Collana

Wiley series in bioinformatics ; ; 3

Classificazione

TEC008000

Altri autori (Persone)

PalSankar K

Disciplina

610.285

Soggetti

Fuzzy systems in medicine

Pattern recognition systems

Bioinformatics

Diagnostic imaging - Data processing

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Frontmatter -- Introduction to Pattern Recognition and Data Mining -- Rough-Fuzzy Hybridization and Granular Computing -- Rough-Fuzzy Clustering: Generalized A-Means Algorithm -- Rough-Fuzzy Granulation and Pattern Classification -- Fuzzy-Rough Feature Selection using -Information Measures -- Rough Fuzzy -Medoids and Amino Acid Sequence Analysis -- Clustering Functionally Similar Genes from Microarray Data -- Selection of Discriminative Genes from Microarray Data -- Segmentation of Brain Magnetic Resonance Images -- Index.

Sommario/riassunto

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing<p>Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in



order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.