Clustering with Instance and Attribute Level Side Information
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferenc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2010-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/2102.pdf |
| _version_ | 1818543405486047232 |
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| author | Jinlong Wang Shunyao Wu Gang Li |
| author_facet | Jinlong Wang Shunyao Wu Gang Li |
| author_sort | Jinlong Wang |
| collection | DOAJ |
| description | Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method. |
| first_indexed | 2024-12-11T22:34:57Z |
| format | Article |
| id | doaj.art-af6542e807ff44b684573ae707081d5a |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T22:34:57Z |
| publishDate | 2010-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-af6542e807ff44b684573ae707081d5a2022-12-22T00:48:01ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832010-12-013610.2991/ijcis.2010.3.6.8Clustering with Instance and Attribute Level Side InformationJinlong WangShunyao WuGang LiSelecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.https://www.atlantis-press.com/article/2102.pdfData miningClusteringSemi-supervised learningConstraints |
| spellingShingle | Jinlong Wang Shunyao Wu Gang Li Clustering with Instance and Attribute Level Side Information International Journal of Computational Intelligence Systems Data mining Clustering Semi-supervised learning Constraints |
| title | Clustering with Instance and Attribute Level Side Information |
| title_full | Clustering with Instance and Attribute Level Side Information |
| title_fullStr | Clustering with Instance and Attribute Level Side Information |
| title_full_unstemmed | Clustering with Instance and Attribute Level Side Information |
| title_short | Clustering with Instance and Attribute Level Side Information |
| title_sort | clustering with instance and attribute level side information |
| topic | Data mining Clustering Semi-supervised learning Constraints |
| url | https://www.atlantis-press.com/article/2102.pdf |
| work_keys_str_mv | AT jinlongwang clusteringwithinstanceandattributelevelsideinformation AT shunyaowu clusteringwithinstanceandattributelevelsideinformation AT gangli clusteringwithinstanceandattributelevelsideinformation |