Comparison of Three Parallel Implementations of an Induction Algorithm

Abstract

Recently, researchers have tried to apply ILP to KDD because ILP enlarges the applicability of Machine Learning to cover KDD and Data Mining: it enables them to learn from multiple relational tables. Many scientific discovery systems are motivated from the desire to deal with larger databases. However the larger the databases are, the more computational power we need. Parallel computing is a possible solution to this problem. This research also aims to implement QUINLAN 's Foil in parallel. Foil finds definitions of relations using other relations in background knowledge with a top-down approach. There are two approaches to designing parallel algorithms for inductive learning, the search space parallel approach and the data parallel approach. In ILP data sets consist of training sets and background knowledge. Thus we examine three approaches, to part the search space, the training set, and the background knowledge. We experimented on FUJITSU AP3000 to compare among these approaches. Ex..