Discovering Compositions

The goal of exploratory data analysis -- or, data mining -- is making sense of data. We develop theory and algorithms that help you understand your data better, with the lofty goal that this helps formulating (better) hypotheses. More in particular, our methods give detailed insight in how data is structured: characterising distributions in easily understandable terms, showing the most informative patterns, associations, correlations, etc.

My talk will consist of three parts. I will start by explaining what is a pattern composition. Simply put, databases often consist of parts, each best characterised by a different set of patterns. Young parents, for example, exhibit different buying behaviour than elderly couples. Both, however, buy bread and milk. A pattern composition jointly characterises the similarities and differences between such components of a database, without redundancy or noise, by including only patterns that are descriptive for the data, and assigning those patterns only to the relevant components of the data.

In the second part of my talk I will go into the more important question of how to discover the pattern composition of a database when all we have is just a single database that has not yet been split into parts. That is, we are after that partitioning of the data by which we can describe it most succinctly using a pattern composition.

In the third part I will make the connection to causal discovery, as in the end that is our real goal.