  The outcome of the explorative data analysis (EDA) phase is vital for
successful data analysis. EDA is more effective when the user interacts with
the system used to carry out the exploration. In the recently proposed paradigm
of iterative data mining the user controls the exploration by inputting
knowledge in the form of patterns observed during the process. The system then
shows the user views of the data that are maximally informative given the
user's current knowledge. Although this scheme is good at showing surprising
views of the data to the user, there is a clear shortcoming: the user cannot
steer the process. In many real cases we want to focus on investigating
specific questions concerning the data. This paper presents the Human Guided
Data Exploration framework, generalising previous research. This framework
allows the user to incorporate existing knowledge into the exploration process,
focus on exploring a subset of the data, and compare different complex
hypotheses concerning relations in the data. The framework utilises a
computationally efficient constrained randomisation scheme. To showcase the
framework, we developed a free open-source tool, using which the empirical
evaluation on real-world datasets was carried out. Our evaluation shows that
the ability to focus on particular subsets and being able to compare hypotheses
are important additions to the interactive iterative data mining process.
