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- Publisher Website: 10.1016/j.patcog.2014.11.003
- Scopus: eid_2-s2.0-84920672964
- WOS: WOS:000348880300035
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Article: Subspace clustering using affinity propagation
Title | Subspace clustering using affinity propagation |
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Authors | |
Keywords | Data clustering Subspace clustering Attribute weighting Affinity propagation |
Issue Date | 2015 |
Citation | Pattern Recognition, 2015, v. 48, n. 4, p. 1455-1464 How to Cite? |
Abstract | © 2014 Elsevier Ltd. All rights reserved. This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experiments on both synthetic data and real data show that the new algorithm outperforms the affinity propagation algorithm in recovering clusters from data. |
Persistent Identifier | http://hdl.handle.net/10722/277015 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 2.732 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Gan, Guojun | - |
dc.contributor.author | Ng, Michael Kwok Po | - |
dc.date.accessioned | 2019-09-18T08:35:21Z | - |
dc.date.available | 2019-09-18T08:35:21Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Pattern Recognition, 2015, v. 48, n. 4, p. 1455-1464 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277015 | - |
dc.description.abstract | © 2014 Elsevier Ltd. All rights reserved. This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experiments on both synthetic data and real data show that the new algorithm outperforms the affinity propagation algorithm in recovering clusters from data. | - |
dc.language | eng | - |
dc.relation.ispartof | Pattern Recognition | - |
dc.subject | Data clustering | - |
dc.subject | Subspace clustering | - |
dc.subject | Attribute weighting | - |
dc.subject | Affinity propagation | - |
dc.title | Subspace clustering using affinity propagation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.patcog.2014.11.003 | - |
dc.identifier.scopus | eid_2-s2.0-84920672964 | - |
dc.identifier.volume | 48 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1455 | - |
dc.identifier.epage | 1464 | - |
dc.identifier.isi | WOS:000348880300035 | - |
dc.identifier.issnl | 0031-3203 | - |