Cargando…

Data Mining and Knowledge Discovery for Big Data Methodologies, Challenge and Opportunities /

The field of data mining has made significant and far-reaching advances over the past three decades.  Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of t...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Chu, Wesley W. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:Studies in Big Data, 1
Temas:
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-642-40837-3
003 DE-He213
005 20220115224709.0
007 cr nn 008mamaa
008 130924s2014 gw | s |||| 0|eng d
020 |a 9783642408373  |9 978-3-642-40837-3 
024 7 |a 10.1007/978-3-642-40837-3  |2 doi 
050 4 |a Q342 
072 7 |a UYQ  |2 bicssc 
072 7 |a TEC009000  |2 bisacsh 
072 7 |a UYQ  |2 thema 
082 0 4 |a 006.3  |2 23 
245 1 0 |a Data Mining and Knowledge Discovery for Big Data  |h [electronic resource] :  |b Methodologies, Challenge and Opportunities /  |c edited by Wesley W. Chu. 
250 |a 1st ed. 2014. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2014. 
300 |a X, 311 p. 99 illus., 29 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Studies in Big Data,  |x 2197-6511 ;  |v 1 
505 0 |a Aspect and Entity Extraction for Opinion Mining -- Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data -- Spatio-Temporal Data Mining for Climate Data: Advances, Challenges -- Mining Discriminative Subgraph Patterns from Structural Data -- Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics -- InfoSearch: A Social Search Engine -- Social Media in Disaster Relief: Usage Patterns, Data Mining Tools, and Current Research Directions -- A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation -- A Clustering Approach to Constrained Binary Matrix Factorization. 
520 |a The field of data mining has made significant and far-reaching advances over the past three decades.  Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease.  Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization. 
650 0 |a Computational intelligence. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence. 
700 1 |a Chu, Wesley W.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783642408380 
776 0 8 |i Printed edition:  |z 9783642408366 
776 0 8 |i Printed edition:  |z 9783662509456 
830 0 |a Studies in Big Data,  |x 2197-6511 ;  |v 1 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-642-40837-3  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712)