Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Communication-efficient distributed mining of association rules
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
Sampling-based sequential subgroup mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On the Tractability of Rule Discovery from Distributed Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Parallel and distributed methods for incremental frequent itemset mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Secure top-k subgroup discovery
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Secure Distributed Subgroup Discovery in Horizontally Partitioned Data
Transactions on Data Privacy
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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 find patterns that point out major differences between individual databases with respect to a specific property of interest (target attribute). We point 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.