Mathisen, AndreasNielsen, MatthiasGrønbæk, KajAnna Puig Puig and Tobias Isenberg2017-06-122017-06-122017978-3-03868-044-4https://doi.org/10.2312/eurp.20171164https://diglib.eg.org:443/handle/10.2312/eurp20171164Recent research shows promise in combining Information Visualization (IV) and Machine Learning (ML) to assist data analysis performed by domain experts. However, this approach presents non-trivial challenges, in particular when the goal is to incorporate knowledge provided by the domain expert in underlying ML algorithms. To address these challenges, we present an analytical process and a visual analytics tool that uses visual queries to capture examples from the domain experts' existing reasoning process which will guide the subsequent clustering. Our work is motivated by a collaboration with personnel at the Danish Business Authority, who are interested in two types of insights: (1) On which data dimensions is a selected subset of companies different from the remaining companies? (2) Which other companies lie within the same multi-dimensional subspace? The poster will illustrate a real analysis scenario, where the presented analytic process allows auditors to use their knowledge of identified "suspicious" companies to kick-start the analysis for others.10.2312/eurp.2017116441-43