A Visual Approach for Interactive Keyterm-Based Clustering

摘要

The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adaptresults 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 inunsupervised 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 viewsof document and cluster relationship . 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 thatreveals overwhelmingly positive acceptance toward keyterm-based clustering.