Parallel Inductive Logic in Data Mining

Data-mining is the process of automatic extraction of novel, useful and understandable patterns from very large databases. High-performance, scalable, and parallel computing algorithms are crucial in data mining as datasets grow inexorably 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 very expressive means of representation: All examples, background knowledge as well as the induced theory are expressed in rst-order logic. However, such an expressive representation is often computationally expensive. This report rst presents the background for parallel data mining, the BSP model, and inductive logic programming. Based on the study, this report gives an approach to parallel inductive logic in data mining that solves the potential performance problem. Both parallel algorithm and cost analysis are provided. This approach is applied to a number of problems and it shows a super-linear speedup. To justify this analysis, I implemented a parallel version of a core ILP system { Progol { in C with the support of the BSP parallel model. Three test cases are provided and a double speedup phenomenon is observed on all these datasets and on two di erent parallel computers.