Association for Computing Machinery
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 in-corporation of the users experience in text clustering tasks by selecting a set of high-level features. In this paper, the authors propose an approach to improve the robustness of consensus clustering using interactive feature selection. They have reported some experimental results on real-world datasets that show the effectiveness of their approach.