Clustering with Instance and Attribute Level Side Information

International Journal of Computational Intelligence Systems

关键词: Data mining;    Clustering;    Semi-supervised learning;    Constraints;   

DOI  :  10.2991/ijcis.2010.3.6.8

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown