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.
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Xu, S., Wang, Z., Li, X., Cao, R. (2013). A Novel Cluster Combination Algorithm for Document Clustering. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_24
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DOI: https://doi.org/10.1007/978-3-642-36669-7_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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