A comparative study on ILP-based concept discovery systems

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.

► For a concept discovery task, the user should consider several points for selecting the most suitable system.
► If the target concept is not explicit, using WARMR is a wise choice in order to see various frequent clauses hidden in the data.
► If the user is familiar with mode declarations, search and evaluation mechanisms, PROGOL and ALEPH are suitable choices.
► For a naive user, who has the data available in the data-base and who does not have information on inner working of concept discovery mechanisms, C2D provides a simple, yet effective mechanism.