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Contains four refereed papers covering important classes of data mining algorithms: classification, clustering, association rule discovery, and learning Bayesian networks. Srivastava et al present a detailed analysis of the parallelization strategy of tree induction algorithms. Xu et al present a parallel clustering algorithm for distributed memory machines. A new scalable algorithm for association rule discovery and a survey of other strategies is covered by Cheung et al. The final paper, written by Xiang et al, describes an algorithm for parallel learning of Bayesian networks. The papers aim to take a practical approach to large scale mining applications and increase useable knowledge concerning high performance computing technology. Lacks a subject index. Annotation c. Book News, Inc., Portland, OR (booknews.com)
High Performance Data Mining: Scaling Algorithms, Applications and Systems
by Yike GuoView All Available Formats & Editions
High Performance Data Mining: Scaling Algorithms, Applications and Sys tems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.See more details below
Overview
High Performance Data Mining: Scaling Algorithms, Applications and Sys tems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
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Product Details
- ISBN-13:
- 9781475784152
- Publisher:
- Springer US
- Publication date:
- 12/31/2013
- Edition description:
- Softcover reprint of the original 1st ed. 2002
- Pages:
- 106
- Product dimensions:
- 6.00(w) x 9.00(h) x 0.24(d)
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