Advances in K-means Clustering

A Data Mining Thinking

Business & Finance, Economics, Statistics, Nonfiction, Computers, Database Management, General Computing
Cover of the book Advances in K-means Clustering by Junjie Wu, Springer Berlin Heidelberg
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Junjie Wu ISBN: 9783642298073
Publisher: Springer Berlin Heidelberg Publication: July 9, 2012
Imprint: Springer Language: English
Author: Junjie Wu
ISBN: 9783642298073
Publisher: Springer Berlin Heidelberg
Publication: July 9, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer Berlin Heidelberg

Cover of the book Algorithms in Bioinformatics by Junjie Wu
Cover of the book Handbook of Polymernanocomposites. Processing, Performance and Application by Junjie Wu
Cover of the book A Theory of Contestation by Junjie Wu
Cover of the book Real Estate Investments in Germany by Junjie Wu
Cover of the book Mosquitoes and Their Control by Junjie Wu
Cover of the book Chuang-Tzu by Junjie Wu
Cover of the book Internet-Ökonomie by Junjie Wu
Cover of the book Magnetothermal Properties near Quantum Criticality in the Itinerant Metamagnet Sr3Ru2O7 by Junjie Wu
Cover of the book NMR — From Spectra to Structures by Junjie Wu
Cover of the book Cardiovascular and Cardiac Therapeutic Devices by Junjie Wu
Cover of the book Soils, Plants and Clay Minerals by Junjie Wu
Cover of the book Cognitive Organisation by Junjie Wu
Cover of the book Chitosan for Biomaterials II by Junjie Wu
Cover of the book Experimentalphysik 3 by Junjie Wu
Cover of the book Realm of Tolerance by Junjie Wu
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy