Parallel Inductive Logic for Data Mining

Data mining is the process of automatic extraction of novel, useful and understandable patterns in very large databases. High-performance, scalable, and parallel computing algorithms are crucial in data mining as datasets grow in size and complexity. Inductive logic is a research area in the intersection of machine learning and logic programming, which has been recently applied to data mining. Inductive logic studies learning from examples, within the framework provided by clausal logic. It provides a uniform and expressive means of representation: examples, background knowledge, and induced theories are all expressed in rst-order logic. Such an expressive representation is computationally expensive, so it is natural to consider improving the performance of inductive logic data mining using parallelism. We present a parallelization technique for inductive logic, and implement a parallel version of a core inductive logic programming system, Progol. Performance results on several datasets and platforms are reported.