Ebook: High Performance Data Mining: Scaling Algorithms, Applications and Systems
- Tags: Data Structures Cryptology and Information Theory, Information Storage and Retrieval, Processor Architectures
- Year: 2002
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
High Performance Data Mining: Scaling Algorithms, Applications andSystems brings together in one place important contributions and up-to-date research results in this fast moving area.
High Performance Data Mining: Scaling Algorithms, Applications andSystems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
High Performance Data Mining: Scaling Algorithms, Applications andSystems brings together in one place important contributions and up-to-date research results in this fast moving area.
High Performance Data Mining: Scaling Algorithms, Applications andSystems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
High Performance Data Mining: Scaling Algorithms, Applications andSystems brings together in one place important contributions and up-to-date research results in this fast moving area.
High Performance Data Mining: Scaling Algorithms, Applications andSystems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Content:
Front Matter....Pages i-iv
Editorial....Pages 235-236
Parallel Formulations of Decision-Tree Classification Algorithms....Pages 237-261
A Fast Parallel Clustering Algorithm for Large Spatial Databases....Pages 263-290
Effect of Data Distribution in Parallel Mining of Associations....Pages 291-314
Parallel Learning of Belief Networks in Large and Difficult Domains....Pages 315-339
High Performance Data Mining: Scaling Algorithms, Applications andSystems brings together in one place important contributions and up-to-date research results in this fast moving area.
High Performance Data Mining: Scaling Algorithms, Applications andSystems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Content:
Front Matter....Pages i-iv
Editorial....Pages 235-236
Parallel Formulations of Decision-Tree Classification Algorithms....Pages 237-261
A Fast Parallel Clustering Algorithm for Large Spatial Databases....Pages 263-290
Effect of Data Distribution in Parallel Mining of Associations....Pages 291-314
Parallel Learning of Belief Networks in Large and Difficult Domains....Pages 315-339
....
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