Clustering with Instance and Attribute Level Side Information

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/).

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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  -