Infrequent Item mining in multiple data streams
The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded whe...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE computer society publishing services
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/8427 |
| _version_ | 1848745655619551232 |
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| author | Saha, Budhaditya Lazarescu, Mihai Venkatesh, Svetha |
| author2 | IEEE computer society |
| author_facet | IEEE computer society Saha, Budhaditya Lazarescu, Mihai Venkatesh, Svetha |
| author_sort | Saha, Budhaditya |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques. |
| first_indexed | 2025-11-14T06:20:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8427 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:20:49Z |
| publishDate | 2007 |
| publisher | IEEE computer society publishing services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-84272017-09-13T14:38:33Z Infrequent Item mining in multiple data streams Saha, Budhaditya Lazarescu, Mihai Venkatesh, Svetha IEEE computer society The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques. 2007 Conference Paper http://hdl.handle.net/20.500.11937/8427 10.1109/ICDMW.2007.32 IEEE computer society publishing services restricted |
| spellingShingle | Saha, Budhaditya Lazarescu, Mihai Venkatesh, Svetha Infrequent Item mining in multiple data streams |
| title | Infrequent Item mining in multiple data streams |
| title_full | Infrequent Item mining in multiple data streams |
| title_fullStr | Infrequent Item mining in multiple data streams |
| title_full_unstemmed | Infrequent Item mining in multiple data streams |
| title_short | Infrequent Item mining in multiple data streams |
| title_sort | infrequent item mining in multiple data streams |
| url | http://hdl.handle.net/20.500.11937/8427 |