Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Interactive feature selection for document clustering
Proceedings of the 2011 ACM Symposium on Applied Computing
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Consensus clustering and interactive feature selection are very useful methods to extract and manage knowledge from texts. While consensus clustering allows the aggregation of different clustering solutions into a single robust clustering solution, the interactive feature selection facilitates the incorporation of the users experience in text clustering tasks by selecting a set of high-level features. In this paper, we propose an approach to improve the robustness of consensus clustering using interactive feature selection. We have reported some experimental results on real-world datasets that show the effectiveness of our approach.