Improving consensus clustering of texts using interactive feature selection

Ricardo M. Marcacini, Marcos Aurélio Domingues, Solange Oliveira Rezende

Abstract: 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.