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.
| ISBN: | 9783642447570 |
| Publication date: | 9th August 2014 |
| Author: | Junjie Wu |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 180 pages |
| Series: | Springer Theses |
| Genres: |
Data mining Expert systems / knowledge-based systems Business mathematics and systems Probability and statistics Business applications Databases Economics, Finance, Business and Management |
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.
Advances in K-Means Clustering features in the following genres: Data mining, Expert systems / knowledge-based systems, Business mathematics and systems, Probability and statistics, Business applications, Databases, Economics, Finance, Business and Management
Advances in K-Means Clustering is available in Paperback, Hardback
Advances in K-Means Clustering was written by Junjie Wu and published by Springer an imprint of Springer Berlin Heidelberg
Advances in K-Means Clustering has 180 pages
Yes it is part of Springer Theses series