A visual approach for interactive keyterm-based clustering

The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adapt results to various applications in text analysis. Its way of markedly influencing the results, for instance, by expressing important terms in relevance order, requires little knowledge of the algorithm and has predictable effect, speeding up the task. This article first presents a text-clustering algorithm that can easily be extended into an interactive algorithm. We evaluate its performance against state-of-the-art clustering algorithms in unsupervised mode. Next, we propose three interactive versions of the algorithm based on keyterm labeling, document labeling, and hybrid labeling. We then demonstrate that keyterm labeling is more effective than document labeling in text clustering. Finally, we propose a visual approach to support the keyterm-based version of the algorithm. Visualizations are provided for the whole collection as well as for detailed views of document and cluster relationships. We show the effectiveness and flexibility of our framework, Vis-Kt, by presenting typical clustering cases on real text document collections. A user study is also reported that reveals overwhelmingly positive acceptance toward keyterm-based clustering.