Improving Consensus Clustering of Texts Using Interactive Feature Selection

Provided by: Association for Computing Machinery
Topic: Data Management
Format: PDF
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.

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