Advances in K-means Clustering: A Data Mining Thinking - Couverture rigide

Wu, JunJie

Couverture rigide

ISBN 10 : 3642298060 ISBN 13 : 9783642298066

Editeur : Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2012

Synopsis

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.

A propos du livre

À propos de l'auteur

Zhongyu Li is the winner of the Outstanding Doctoral Society Award of the Chinese Institute of Electronics, and he was successfully selected for the China Postdoctoral Innovative Talent Support Program. He received the B.S. and Ph.D. degrees in Electronic Engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2011 and 2017, respectively. From 2015 to 2016, he was a visiting Ph.D. student with the Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy. He is currently an associate professor with UESTC. His research interests include synthetic aperture radar technology (particular emphasis on bistatic/multi-static SAR moving target detection and imaging technologies). Junjie Wu is the winner of the Outstanding Youth Fund of the National Natural Science Foundation of China. He received the B.S., M.S., and Ph.D. degrees in electronic engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2004, 2007, and 2013, respectively. He is currently a professor with the UESTC, Chengdu. From January 2012 to January 2013, he was a visiting student with the Department of Electrical and Computer Engineering, Duke University, USA. His research interests include synthetic aperture radar imaging (particular emphasis on bistatic synthetic aperture radar). Jianyu Yang is a fellow of the Chinese Institute of Electronics. He received the B.S. degree in electronic engineering from the National University of Defense Technology, Changsha, China, in 1984 and the M.S. and Ph.D. degrees in electronic engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 1987 and 1991, respectively. He is currently a professor with the School of Information and Communication Engineering, UESTC. His research interests are mainly in synthetic aperture radar (SAR) and statistical signal processing. Zhutian Liu: Zhutian Liu received the B.S. degree in electronic engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2015, where he is pursuing the Ph.D. degree with the School of Information and Communication Engineering. His research interests include synthetic aperture radar clutter suppression and ground moving target detection.

À propos de la quatrième de couverture

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.

Détails de l'édition

• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Date d'édition: 2012
• Langue: anglais
• ISBN 10: 3642298060
• ISBN 13: 9783642298066
• Reliure: Relié
• Nombre de pages: 178
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne