Semi-supervised clustering with multi-viewpoint based similarity measure
The traditional (dis)similarity measure between a pair of data objects in a clustering method uses only a single viewpoint, which is usually the origin as the only reference point. Recently a novel multi-viewpoint based similarity (MVS) measure [1] has been proposed, which utilizes many different vi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97853 http://hdl.handle.net/10220/12397 |
| _version_ | 1826116395418517504 |
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| author | Yan, Yang Chen, Lihui Nguyen, Duc Thang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yan, Yang Chen, Lihui Nguyen, Duc Thang |
| author_sort | Yan, Yang |
| collection | NTU |
| description | The traditional (dis)similarity measure between a pair of data objects in a clustering method uses only a single viewpoint, which is usually the origin as the only reference point. Recently a novel multi-viewpoint based similarity (MVS) measure [1] has been proposed, which utilizes many different viewpoints in similarity measure and it has been successfully applied in data clustering. In this paper, we study how a semi-supervised MVS-based clustering can be developed by incorporating some prior knowledge in the form of class labels, when they are available to the user. A novel search-based semi-supervised clustering method called CMVS is proposed in the MVS manner with the help of a small percentage of objects being labeled. Two new criterion functions for clustering have been formulated accordingly, when only these labeled objects are considered as the viewpoints in the multi-viewpoints based similarity measure. Theoretical discussion has been conducted to ensure the newly proposed criterion functions make good use of the prior knowledge in terms of similarity measure, besides seeding. Empirical study is performed on various benchmark datasets to demonstrate the effectiveness and verify the merit of our proposed semi-supervised MVS clustering. |
| first_indexed | 2024-10-01T04:11:01Z |
| format | Conference Paper |
| id | ntu-10356/97853 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:11:01Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/978532020-03-07T13:24:48Z Semi-supervised clustering with multi-viewpoint based similarity measure Yan, Yang Chen, Lihui Nguyen, Duc Thang School of Electrical and Electronic Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Electrical and electronic engineering The traditional (dis)similarity measure between a pair of data objects in a clustering method uses only a single viewpoint, which is usually the origin as the only reference point. Recently a novel multi-viewpoint based similarity (MVS) measure [1] has been proposed, which utilizes many different viewpoints in similarity measure and it has been successfully applied in data clustering. In this paper, we study how a semi-supervised MVS-based clustering can be developed by incorporating some prior knowledge in the form of class labels, when they are available to the user. A novel search-based semi-supervised clustering method called CMVS is proposed in the MVS manner with the help of a small percentage of objects being labeled. Two new criterion functions for clustering have been formulated accordingly, when only these labeled objects are considered as the viewpoints in the multi-viewpoints based similarity measure. Theoretical discussion has been conducted to ensure the newly proposed criterion functions make good use of the prior knowledge in terms of similarity measure, besides seeding. Empirical study is performed on various benchmark datasets to demonstrate the effectiveness and verify the merit of our proposed semi-supervised MVS clustering. 2013-07-26T06:49:04Z 2019-12-06T19:47:22Z 2013-07-26T06:49:04Z 2019-12-06T19:47:22Z 2012 2012 Conference Paper Yan, Y., Chen, L., & Nguyen, D. T. (2012). Semi-supervised clustering with multi-viewpoint based similarity measure. The 2012 International Joint Conference on Neural Networks (IJCNN). https://hdl.handle.net/10356/97853 http://hdl.handle.net/10220/12397 10.1109/IJCNN.2012.6252650 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yan, Yang Chen, Lihui Nguyen, Duc Thang Semi-supervised clustering with multi-viewpoint based similarity measure |
| title | Semi-supervised clustering with multi-viewpoint based similarity measure |
| title_full | Semi-supervised clustering with multi-viewpoint based similarity measure |
| title_fullStr | Semi-supervised clustering with multi-viewpoint based similarity measure |
| title_full_unstemmed | Semi-supervised clustering with multi-viewpoint based similarity measure |
| title_short | Semi-supervised clustering with multi-viewpoint based similarity measure |
| title_sort | semi supervised clustering with multi viewpoint based similarity measure |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/97853 http://hdl.handle.net/10220/12397 |
| work_keys_str_mv | AT yanyang semisupervisedclusteringwithmultiviewpointbasedsimilaritymeasure AT chenlihui semisupervisedclusteringwithmultiviewpointbasedsimilaritymeasure AT nguyenducthang semisupervisedclusteringwithmultiviewpointbasedsimilaritymeasure |