Efficiently mining frequent patterns from dense datasets using a cluster of computers
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The d...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2003
|
| Online Access: | http://hdl.handle.net/20.500.11937/17116 |
| _version_ | 1848749371992047616 |
|---|---|
| author | Rudra, Amit Gopalan, Raj Sucahyo, Yudho |
| author2 | Gedeon, Tamas D. |
| author_facet | Gedeon, Tamas D. Rudra, Amit Gopalan, Raj Sucahyo, Yudho |
| author_sort | Rudra, Amit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The data needs to be distributed to the nodes of the cluster only once and the mining can be performed in parallel many times with different parameter settings for minimum support. The algorithm is based on a master-slave scheme where a coordinator controls the data parallel programs running on a number of nodes of the cluster. The parallel program was executed on a cluster of Alpha SMPs. The performance of the algorithm was studied on small and large dense datasets. We report the results of the experiments that show both speed up and scale up of our algorithm along with our conclusions and pointers for further work. |
| first_indexed | 2025-11-14T07:19:53Z |
| format | Book Chapter |
| id | curtin-20.500.11937-17116 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:19:53Z |
| publishDate | 2003 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-171162022-10-06T03:19:40Z Efficiently mining frequent patterns from dense datasets using a cluster of computers Rudra, Amit Gopalan, Raj Sucahyo, Yudho Gedeon, Tamas D. Fung, Lance C.C. Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The data needs to be distributed to the nodes of the cluster only once and the mining can be performed in parallel many times with different parameter settings for minimum support. The algorithm is based on a master-slave scheme where a coordinator controls the data parallel programs running on a number of nodes of the cluster. The parallel program was executed on a cluster of Alpha SMPs. The performance of the algorithm was studied on small and large dense datasets. We report the results of the experiments that show both speed up and scale up of our algorithm along with our conclusions and pointers for further work. 2003 Book Chapter http://hdl.handle.net/20.500.11937/17116 10.1007/b94701 Springer fulltext |
| spellingShingle | Rudra, Amit Gopalan, Raj Sucahyo, Yudho Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title | Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title_full | Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title_fullStr | Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title_full_unstemmed | Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title_short | Efficiently mining frequent patterns from dense datasets using a cluster of computers |
| title_sort | efficiently mining frequent patterns from dense datasets using a cluster of computers |
| url | http://hdl.handle.net/20.500.11937/17116 |