Distributed Subgroup Mining

Subgroup discovery is a popular form of supervised rule learning, applicable to descriptive and predictive tasks. In this work we study two natural extensions of classical subgroup discovery to distributed settings. In the first variant the goal is to efficiently identify global subgroups, i.e. the rules an analysis would yield after collecting all the data at a single central database. In contrast, the second considered variant takes the locality of data explicitly into account. The aim is to findpatterns that point out major differences between individual databases with respect to a specific property of interest (target attribute).Wepoint out substantial differences between these novel learning problems and other kinds of distributed data mining tasks. These differences motivate new search and communication strategies, aiming at a minimization of computation time and communication costs. We present and empirically evaluate new algorithms for both considered variants.