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Item Details
| Title:
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GROUPING MULTIDIMENSIONAL DATA
RECENT ADVANCES IN CLUSTERING |
| By: |
Jacob Kogan (Editor), Charles Nicholas (Editor), Marc Teboulle (Editor) |
| Format: |
Hardback |
| List price:
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£109.99 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
354028348X |
| ISBN 13: |
9783540283485 |
| Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
| Pub. date: |
16 December, 2005 |
| Pages: |
268 |
| Description: |
Addresses topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. This volume covers theoretical basics as well as application and evaluation of algorithms. |
| Synopsis: |
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas. |
| Illustrations: |
36 Tables, black and white; XII, 268 p. |
| Publication: |
Germany |
| Imprint: |
Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
| Returns: |
Returnable |
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