A comparative study on ILP-based concept discovery systems

摘要

Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory information about ILP is given, and then the above-mentioned systems and an ILP-based concept discovery system called C2D are briefly described and the fundamentals of their mechanisms are demonstrated on a running example. Finally, a set of experimental results on real-world problems are presented in order to evaluate and compare the performance of the above-mentioned systems.