Advances in K-means Clustering A Data Mining Thinking /

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establis...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wu, Junjie (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:Springer Theses, Recognizing Outstanding Ph.D. Research,
Temas:
Data mining.
Statistics .
Business information services.
Database management.
Data Mining and Knowledge Discovery.
Statistics in Business, Management, Economics, Finance, Insurance.
IT in Business.
Database Management.
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-642-29807-3
003 DE-He213
005 20220116011119.0
007 cr nn 008mamaa
008 120709s2012 gw | s |||| 0|eng d
020 |a 9783642298073  |9 978-3-642-29807-3 
024 7 |a 10.1007/978-3-642-29807-3  |2 doi 
050 4 |a QA76.9.D343 
072 7 |a UNF  |2 bicssc 
072 7 |a UYQE  |2 bicssc 
072 7 |a COM021030  |2 bisacsh 
072 7 |a UNF  |2 thema 
072 7 |a UYQE  |2 thema 
082 0 4 |a 006.312  |2 23 
100 1 |a Wu, Junjie.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Advances in K-means Clustering  |h [electronic resource] :  |b A Data Mining Thinking /  |c by Junjie Wu. 
250 |a 1st ed. 2012. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2012. 
300 |a XVI, 180 p.  |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 Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5061 
505 0 |a Cluster Analysis and K-means Clustering: An Introduction -- The Uniform Effect of K-means Clustering -- Generalizing Distance Functions for Fuzzy c-Means Clustering -- Information-Theoretic K-means for Text Clustering -- Selecting External Validation Measures for K-means Clustering -- K-means Based Local Decomposition for Rare Class Analysis -- K-means Based Consensus Clustering. 
520 |a Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China. 
650 0 |a Data mining. 
650 0 |a Statistics . 
650 0 |a Business information services. 
650 0 |a Database management. 
650 1 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Statistics in Business, Management, Economics, Finance, Insurance. 
650 2 4 |a IT in Business. 
650 2 4 |a Database Management. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783642298080 
776 0 8 |i Printed edition:  |z 9783642447570 
776 0 8 |i Printed edition:  |z 9783642298066 
830 0 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5061 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-642-29807-3  |z Texto Completo 
912 |a ZDB-2-SCS 
912 |a ZDB-2-SXCS 
950 |a Computer Science (SpringerNature-11645) 
950 |a Computer Science (R0) (SpringerNature-43710) 

Ejemplares similares