A fusion approach to cluster labeling

We present a novel approach to the cluster labeling task using fusion methods. The core idea of our approach is to weigh labels, suggested by any labeler, according to the estimated labeler's decisiveness with respect to each of its suggested labels. We hypothesize that, a cluster labeler's labeling choice for a given cluster should remain stable even in the presence of a slightly incomplete cluster data. Using state-of-the-art cluster labeling and data fusion methods, evaluated over a large data collection of clusters, we demonstrate that, overall, the cluster labeling fusion methods that further consider the labeler's decisiveness provide the best labeling performance.