Advances in K-Means Clustering A Data Mining Thinking
| Author/creator | Wu, JunJie Author |
| Format | Electronic |
| Publication Info | New York : Springer |
| Description | xvi, 178 p. ill 23.500 x 015.500 cm. |
| Supplemental Content | Full text available from Springer Nature - Springer Computer Science eBooks 2012 English International |
| Supplemental Content | Full text available from Springer Books |
| Subjects |
| Series | Springer Theses Ser. |
| Summary | Annotation 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. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| ISBN | 9783642298066 |
| ISBN | 3642298060 (Trade Cloth) Active Record |
| Standard identifier# | 9783642298066 |
| Stock number | 9783642298066 00024965 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |