| 期刊全称 | Advances in K-means Clustering | | 期刊简称 | A Data Mining Thinki | | 影响因子2023 | Junjie Wu | | 视频video | | | 发行地址 | Gives an overall picture on how to adapt K-means to the clustering of newly emerging big data.Establishes a theoretical framework for K-means clustering and cluster validity.Studies the dangerous unif | | 学科分类 | Springer Theses | | 图书封面 |  | | 影响因子 | .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. . | | Pindex | Book 2012 |
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发表于 2025-3-21 19:48:37
