A local approach of adaptive affinity propagation clustering for large scale data

Changyin Sun; Chenghong Wang; Su Song; Yifan Wang

doi:10.1109/IJCNN.2009.5178601

A local approach of adaptive affinity propagation clustering for large scale data

Changyin Sun
, Chenghong Wang
, Su Song
, Yifan Wang

Hohai University
National Natural Science Foundation of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.

Original language	English
Title of host publication	2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages	2998-3002
Number of pages	5
DOIs	https://doi.org/10.1109/IJCNN.2009.5178601
State	Published - 2009
Externally published	Yes
Event	2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States Duration: 14 Jun 2009 → 19 Jun 2009

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/Territory	United States
City	Atlanta, GA
Period	14/06/09 → 19/06/09

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10.1109/IJCNN.2009.5178601

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@inproceedings{014cf1bfadf14e83aecf2d29b8356555,

title = "A local approach of adaptive affinity propagation clustering for large scale data",

abstract = "Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.",

author = "Changyin Sun and Chenghong Wang and Su Song and Yifan Wang",

year = "2009",

doi = "10.1109/IJCNN.2009.5178601",

language = "英语",

isbn = "9781424435531",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "2998--3002",

booktitle = "2009 International Joint Conference on Neural Networks, IJCNN 2009",

note = "2009 International Joint Conference on Neural Networks, IJCNN 2009 ; Conference date: 14-06-2009 Through 19-06-2009",

}

Sun, C, Wang, C, Song, S & Wang, Y 2009, A local approach of adaptive affinity propagation clustering for large scale data. in 2009 International Joint Conference on Neural Networks, IJCNN 2009., 5178601, Proceedings of the International Joint Conference on Neural Networks, pp. 2998-3002, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 14/06/09. https://doi.org/10.1109/IJCNN.2009.5178601

A local approach of adaptive affinity propagation clustering for large scale data. / Sun, Changyin; Wang, Chenghong; Song, Su et al.
2009 International Joint Conference on Neural Networks, IJCNN 2009. 2009. p. 2998-3002 5178601 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Sun, Changyin

AU - Wang, Chenghong

AU - Song, Su

AU - Wang, Yifan

PY - 2009

Y1 - 2009

N2 - Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.

AB - Affinity propagation exhibits fast execution speed and finds clusters with low error rate when clustering sparsely related data but its values of parameters are fixed. This paper proposes a modified method named partition adaptive affinity propagation, which can automatically eliminate oscillations and adjust the values of parameters when rerunning affinity propagation procedure to yield optimal clustering results, with high execution speed and precision. Experiments are carried on UCI datasets and CaltechlOl dataset, and ORL faces dataset. The results verify that this adaptive method is effective and feasible.

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DO - 10.1109/IJCNN.2009.5178601

M3 - 会议稿件

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SN - 9781424435531

T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009

T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009

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ER -

A local approach of adaptive affinity propagation clustering for large scale data

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