Volume 3, Issue 6, December 2010, Pages 770 - 785
Clustering with Instance and Attribute Level Side Information
Authors
Jinlong Wang, Shunyao Wu, Gang Li
Corresponding Author
Jinlong Wang
Received 16 February 2010, Accepted 26 October 2010, Available Online 1 December 2010.
- DOI
- 10.2991/ijcis.2010.3.6.8How to use a DOI?
- Keywords
- Data mining, Clustering, Semi-supervised learning, Constraints
- Abstract
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.
- Copyright
- © 2010, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Jinlong Wang AU - Shunyao Wu AU - Gang Li PY - 2010 DA - 2010/12/01 TI - Clustering with Instance and Attribute Level Side Information JO - International Journal of Computational Intelligence Systems SP - 770 EP - 785 VL - 3 IS - 6 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2010.3.6.8 DO - 10.2991/ijcis.2010.3.6.8 ID - Wang2010 ER -