The emerging field of visual analytics changes the way we model, gather, and analyze data. Current data analysis approaches suggest to gather as much data as possible and then focus on goal and process oriented data analysis techniques. Visual analytics changes this approach and the methodology to interpret the results becomes the key issue. This paper contributes with a method to interpret visual hierarchical heavy hitters (VHHHs). We show how to analyze data on the general level and how to examine specific areas of the data. We identify five common patterns that build the interpretation alphabet of VHHHs. We demonstrate our method on three different real world datasets and show the effectiveness of our approach.