Fast discovery of association rules
Advances in knowledge discovery and data mining
Algorithmic Program DeBugging
Discovery of relational association rules
Relational Data Mining
Learning Logical Definitions from Relations
Machine Learning
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Prospects and challenges for multi-relational data mining
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
QG/GA: a stochastic search for Progol
Machine Learning
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Analyzing Transitive Rules on a Hybrid Concept Discovery System
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
From inductive logic programming to relational data mining
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Fitness function based on binding and recall rate for genetic inductive logic programming
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Hi-index | 12.05 |
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 C^2D 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.