A novel cluster combination algorithm for document clustering

Authors:
Sen Xu;Zhenggang Wang;Xianfeng Li;Rui Cao
Affiliations:
Pattern Recognition and Data Mining Lab, Yancheng Institute of Technology, Yancheng, China;Pattern Recognition and Data Mining Lab, Yancheng Institute of Technology, Yancheng, China;Pattern Recognition and Data Mining Lab, Yancheng Institute of Technology, Yancheng, China;Pattern Recognition and Data Mining Lab, Yancheng Institute of Technology, Yancheng, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

Ensemble techniques have been successfully applied in the supervised machine learning area to increase the accuracy and stability of base learner. Recently, analogous techniques have been investigated in unsupervised machine learning area. Research has showed that, by combining an ensemble of multiple clusterings, a superior solution can be attained. In this paper, we solve the cluster combination problem in term of finding a "best" subspace and formulate it as an optimization problem. Then, we get the solution according to basic concept and theorem in linear algebra whereupon a novel cluster combination algorithm is proposed. We compare our algorithm with other common cluster ensemble algorithms on real-world datasets. Experimental results demonstrate the effectiveness of our algorithm.