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Junjie Wu - Advances in K-Means Clustering - 9783642447570 - V9783642447570
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Advances in K-Means Clustering

€ 121.61
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Description for Advances in K-Means Clustering Paperback. The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of 'big data', detailing a theoretical framework for aggregating and validating clusters with K-means. Series: Springer Theses. Num Pages: 196 pages, biography. BIC Classification: KJQ; PBT; UNF. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 11. Weight in Grams: 308.

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 ... Read more

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Product Details

Format
Paperback
Publication date
2014
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
196
Condition
New
Series
Springer Theses
Number of Pages
180
Place of Publication
Berlin, Germany
ISBN
9783642447570
SKU
V9783642447570
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

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