Emergent intertransaction association rules for abnormality detection in intelligent environments
This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are a...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE Computer Society.
2005
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| Online Access: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1595603 http://hdl.handle.net/20.500.11937/25016 |
| _version_ | 1848751589571952640 |
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| author | Luhr, Sebastian Venkatesh, Svetha West, Geoffrey |
| author2 | Palaniswami, M. |
| author_facet | Palaniswami, M. Luhr, Sebastian Venkatesh, Svetha West, Geoffrey |
| author_sort | Luhr, Sebastian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining. |
| first_indexed | 2025-11-14T07:55:08Z |
| format | Conference Paper |
| id | curtin-20.500.11937-25016 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:55:08Z |
| publishDate | 2005 |
| publisher | IEEE Computer Society. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-250162017-01-30T12:46:18Z Emergent intertransaction association rules for abnormality detection in intelligent environments Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Palaniswami, M. This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining. 2005 Conference Paper http://hdl.handle.net/20.500.11937/25016 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1595603 IEEE Computer Society. fulltext |
| spellingShingle | Luhr, Sebastian Venkatesh, Svetha West, Geoffrey Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title | Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title_full | Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title_fullStr | Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title_full_unstemmed | Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title_short | Emergent intertransaction association rules for abnormality detection in intelligent environments |
| title_sort | emergent intertransaction association rules for abnormality detection in intelligent environments |
| url | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1595603 http://hdl.handle.net/20.500.11937/25016 |