Incremental clustering of dynamic data streams using connectivity based representative points
We present an incremental graph-based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the m...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Elsevier Science Publishers B. V. Amsterdam
2009
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| Online Access: | http://portal.acm.org/citation.cfm?id=1464905 http://hdl.handle.net/20.500.11937/37647 |
| _version_ | 1848755106399387648 |
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| author | Luhr, Sebastian Lazarescu, Mihai |
| author_facet | Luhr, Sebastian Lazarescu, Mihai |
| author_sort | Luhr, Sebastian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We present an incremental graph-based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the method we propose utilises representative points to both incrementally cluster new data and to selectively retain important cluster information within a knowledge repository. The repository can then be subsequently used to assist in the processing of new data, the archival of critical features for off-line analysis, and in the identification of recurrent patterns. |
| first_indexed | 2025-11-14T08:51:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-37647 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:51:02Z |
| publishDate | 2009 |
| publisher | Elsevier Science Publishers B. V. Amsterdam |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-376472017-09-13T14:02:53Z Incremental clustering of dynamic data streams using connectivity based representative points Luhr, Sebastian Lazarescu, Mihai We present an incremental graph-based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the method we propose utilises representative points to both incrementally cluster new data and to selectively retain important cluster information within a knowledge repository. The repository can then be subsequently used to assist in the processing of new data, the archival of critical features for off-line analysis, and in the identification of recurrent patterns. 2009 Journal Article http://hdl.handle.net/20.500.11937/37647 10.1016/j.datak.2008.08.006 http://portal.acm.org/citation.cfm?id=1464905 Elsevier Science Publishers B. V. Amsterdam restricted |
| spellingShingle | Luhr, Sebastian Lazarescu, Mihai Incremental clustering of dynamic data streams using connectivity based representative points |
| title | Incremental clustering of dynamic data streams using connectivity based representative points |
| title_full | Incremental clustering of dynamic data streams using connectivity based representative points |
| title_fullStr | Incremental clustering of dynamic data streams using connectivity based representative points |
| title_full_unstemmed | Incremental clustering of dynamic data streams using connectivity based representative points |
| title_short | Incremental clustering of dynamic data streams using connectivity based representative points |
| title_sort | incremental clustering of dynamic data streams using connectivity based representative points |
| url | http://portal.acm.org/citation.cfm?id=1464905 http://hdl.handle.net/20.500.11937/37647 |