Maximum margin clustering on evolutionary data
Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/97733 http://hdl.handle.net/10220/12286 |
| _version_ | 1826127517399908352 |
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| author | Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew Soon |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew Soon |
| author_sort | Fan, Xuhui |
| collection | NTU |
| description | Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data. |
| first_indexed | 2024-10-01T07:09:51Z |
| format | Conference Paper |
| id | ntu-10356/97733 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:09:51Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/977332020-05-28T07:18:09Z Maximum margin clustering on evolutionary data Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew Soon School of Computer Engineering International conference on Information and knowledge management (21st : 2012 : Maui, USA) Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data. 2013-07-25T07:46:59Z 2019-12-06T19:45:57Z 2013-07-25T07:46:59Z 2019-12-06T19:45:57Z 2012 2012 Conference Paper Fan, X., Zhu, L., Cao, L., Cui, X., & Ong, Y.-S. (2012). Maximum margin clustering on evolutionary data. Proceedings of the 21st ACM international conference on Information and knowledge management. https://hdl.handle.net/10356/97733 http://hdl.handle.net/10220/12286 10.1145/2396761.2396842 en © 2012 ACM. |
| spellingShingle | Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew Soon Maximum margin clustering on evolutionary data |
| title | Maximum margin clustering on evolutionary data |
| title_full | Maximum margin clustering on evolutionary data |
| title_fullStr | Maximum margin clustering on evolutionary data |
| title_full_unstemmed | Maximum margin clustering on evolutionary data |
| title_short | Maximum margin clustering on evolutionary data |
| title_sort | maximum margin clustering on evolutionary data |
| url | https://hdl.handle.net/10356/97733 http://hdl.handle.net/10220/12286 |
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