  Visual analytics systems such as Tableau are increasingly popular for
interactive data exploration. These tools, however, do not currently assist
users with detecting or resolving potential data quality problems including the
well-known deduplication problem. Recent approaches for deduplication focus on
cleaning entire datasets and commonly require hundreds to thousands of user
labels. In this paper, we address the problem of deduplication in the context
of visual data analytics. We present a new approach for record deduplication
that strives to produce the cleanest view possible with a limited budget for
data labeling. The key idea behind our approach is to consider the impact that
individual tuples have on a visualization and to monitor how the view changes
during cleaning. With experiments on nine different visualizations for two
real-world datasets, we show that our approach produces significantly cleaner
views for small labeling budgets than state-of-the-art alternatives and that it
also stops the cleaning process after requesting fewer labels.
