402 citations · 913 across the 36 of their papers we have counts for
8 papers · 1 filter
Towards Plausible Graph Anonymization
Yang Zhang, Mathias Humbert, Bartlomiej Surma +3
Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs con…
Telling Cause from Effect using MDL-based Local and Global Regression
Alexander Marx, Jilles Vreeken
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables and from observational data. The two-variable case is…
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
Janis Kalofolias, Mario Boley, Jilles Vreeken
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populatio…
Discovering Reliable Approximate Functional Dependencies
Panagiotis Mandros, Mario Boley, Jilles Vreeken
Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other att…
Causal Inference by Stochastic Complexity
Kailash Budhathoki, Jilles Vreeken
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov co…
Causal Inference on Multivariate and Mixed-Type Data
Alexander Marx, Jilles Vreeken
Given data over the joint distribution of two random variables and , we consider the problem of inferring the most likely causal direction between and . In particular…
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
Apratim Bhattacharyya, Jilles Vreeken
Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe th…
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli +1
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or…