The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
High Performance Cluster Computing: Programming and Applications
High Performance Cluster Computing: Programming and Applications
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
New Algorithms for Efficient Mining of Association Rules
FRONTIERS '99 Proceedings of the The 7th Symposium on the Frontiers of Massively Parallel Computation
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Knowledge discovery is a time-consuming and space intensive endeavor. By distributing such an endeavor, we can diminish both time and space. System INDED(pronounced "indeed") is an inductive implementation that performs rule discovery using the techniques of inductive logic programming and accumulates and handles knowledge using a deductive nonmonotonic reasoning engine. We present four schemes of transforming this large serial inductive logic programming (ILP) knowledge-based discovery system into a distributed ILP discovery system running on a Beowulf cluster. We also present our data partitioning algorithm based on locality used to accomplish the data decomposition used in the scenarios.