Explanatory Data Analysis
Useful insights from data
Our main focus is on exploratory data analysis, often in the form of discovering novel and unexpected patterns that may give useful insights. Even when predictive modelling is the final goal, it is essential to first get a solid grasp on the data. Regardless of the analysis task, we aim for algorithms that are accurate, provide interpretable results, and can be guided by the analyst (to exploit prior knowledge).
Our research builds on the state of the art in information theoretic data mining (e.g., using the minimum description length (MDL) and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics. More broadly speaking, our research can be situated in the fields of data mining, machine learning, data science, and artificial intelligence (AI).
We mostly work on multidisciplinary data science projects, as we strongly believe that this leads to interesting, fundamental data mining problems for which the solutions we propose have the potential to positively impact society.
News and updates
20 September 2018
Sepinoud Azimi is visiting the EDA group for one and a half month. Welcome!
Start of '18-'19
3 September 2018
Welcome to all new students! The second edition of our Master's course on Information Theoretic Data Mining will start next Thursday.