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- Publisher Website: 10.1016/j.patcog.2015.05.016
- Scopus: eid_2-s2.0-84937815800
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Article: Subspace clustering with automatic feature grouping
Title | Subspace clustering with automatic feature grouping |
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Authors | |
Keywords | Subspace clustering k-means Feature group Data clustering |
Issue Date | 2015 |
Citation | Pattern Recognition, 2015, v. 48, n. 11, p. 3703-3713 How to Cite? |
Abstract | © 2015 Elsevier Ltd. All rights reserved. This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG-k-means algorithm in terms of accuracy and choice of parameters. |
Persistent Identifier | http://hdl.handle.net/10722/277024 |
ISSN | 2021 Impact Factor: 8.518 2020 SCImago Journal Rankings: 1.492 |
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 | 2019-09-18T08:35:22Z | - |
dc.date.available | 2019-09-18T08:35:22Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Pattern Recognition, 2015, v. 48, n. 11, p. 3703-3713 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277024 | - |
dc.description.abstract | © 2015 Elsevier Ltd. All rights reserved. This paper proposes a subspace clustering algorithm with automatic feature grouping for clustering high-dimensional data. In this algorithm, a new component is introduced into the objective function to capture the feature groups and a new iterative process is defined to optimize the objective function so that the features of high-dimensional data are grouped automatically. Experiments on both synthetic data and real data show that the new algorithm outperforms the FG-k-means algorithm in terms of accuracy and choice of parameters. | - |
dc.language | eng | - |
dc.relation.ispartof | Pattern Recognition | - |
dc.subject | Subspace clustering | - |
dc.subject | k-means | - |
dc.subject | Feature group | - |
dc.subject | Data clustering | - |
dc.title | Subspace clustering with automatic feature grouping | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.patcog.2015.05.016 | - |
dc.identifier.scopus | eid_2-s2.0-84937815800 | - |
dc.identifier.volume | 48 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 3703 | - |
dc.identifier.epage | 3713 | - |
dc.identifier.isi | WOS:000359028900033 | - |
dc.identifier.issnl | 0031-3203 | - |