Interactive textual feature selection for consensus clustering

• An approach to incorporate users’ experience into consensus clustering is proposed.
• The approach relies on interactive feature selection from textual data.
• We model an additional (high-level) text representation using the selected features.
• We explore high-level features to improve the consensus clustering accuracy.
• Our approach is competitive even when only few features are selected by the users.

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 the clustering tasks by selecting a set of textual features, i.e., including user’s supervision at the term-level. We propose an approach for incorporating interactive textual feature selection into consensus clustering. Experimental results on several text collections demonstrate that our approach significantly improves consensus clustering accuracy, even when only few textual features are selected by the users.