An Effective Semi-Supervised Clustering Framework Integrating Pairwise Constraints and Attribute Preferences

Authors

  • Jinlong Wang School of Computer Engineering, Qingdao Technological University, 266033 Qingdao
  • Shunyao Wu School of Computer Engineering, Qingdao Technological University, 266033 Qingdao
  • Can Wen School of Computer Engineering, Qingdao Technological University, 266033 Qingdao
  • Gang Li School of Computer Engineering, Qingdao Technological University, 266033 Qingdao

Keywords:

Semi-supervised clustering, pairwise, attribute preference

Abstract

Semi-supervised clustering, pairwise, attribute preference
Both the instance level knowledge and the attribute level knowledge can improve clustering quality, but how to effectively utilize both of them is an essential problem to solve. This paper proposes a wrapper framework for semi-supervised clustering, which aims to gracely integrate both kinds of priori knowledge in the clustering process, the instance level knowledge in the form of pairwise constraints and the attribute level knowledge in the form of attribute order preferences. The wrapped algorithm is then designed as a semi-supervised clustering process which transforms this clustering problem into an optimization problem. The experimental results demonstrate the effectiveness and potential of proposed method.

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Published

2012-08-10

How to Cite

Wang, J., Wu, S., Wen, C., & Li, G. (2012). An Effective Semi-Supervised Clustering Framework Integrating Pairwise Constraints and Attribute Preferences. Computing and Informatics, 31(3), 597–612. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/1010