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 Ph D theses per year in China.
Junjie Wu
Advances in K-means Clustering [PDF ebook]
A Data Mining Thinking
Advances in K-means Clustering [PDF ebook]
A Data Mining Thinking
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Ngôn ngữ Anh ● định dạng PDF ● Trang 180 ● ISBN 9783642298073 ● Kích thước tập tin 4.3 MB ● Nhà xuất bản Springer Berlin ● Thành phố Heidelberg ● Quốc gia DE ● Được phát hành 2012 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 2650822 ● Sao chép bảo vệ DRM xã hội