Parallel Mining Association Rules by Thomas Wessel (6 results)

Taschenbuch. Condition: Neu. Parallel Mining of Association Rules Using a Lattice Based Approach | Mining of Association Rules | Wessel Thomas | Taschenbuch | 252 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846502068 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condition: Brand New. 252 pages. 8.66x5.91x0.57 inches. In Stock.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. The goal of this research was to develop and implement a parallel algorithm for mining association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. We implemented the DDRM using a dynamic load balancing approach to assign classes to processors for analysis of these classes in order to determine if there are any rules present in them. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and Partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup. 252 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Thomas WesselDr. Thomas is a Principal Lecturer in the School of Computing and Information Technology, University of Technology, Jamaica. His research interests include data mining, parallel algorithm design, computer graphics and h.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. The goal of this research was to develop and implement a parallel algorithm for mining association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. We implemented the DDRM using a dynamic load balancing approach to assign classes to processors for analysis of these classes in order to determine if there are any rules present in them. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and Partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 252 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. The goal of this research was to develop and implement a parallel algorithm for mining association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. We implemented the DDRM using a dynamic load balancing approach to assign classes to processors for analysis of these classes in order to determine if there are any rules present in them. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and Partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup.