New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Algorithmic Program DeBugging
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Discovery of relational association rules
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Classification of Individuals with Complex Structure
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
| Hi-index | 0.00 |
Multi-relational data mining (MRDM) is a form of data mining operating on data stored in multiple database tables. While machine learning and data mining are traditionally concerned with learning from single tables, MRDM is required in domains where the data are highly structured. One approach to MRDM is to use a predicate-logical language like clausal logic or Prolog to represent and reason about structured objects, an approach which came to be known as inductive logic programming (ILP) [18, 19, 15, 16, 13, 17, 2, 5]. In this talk I will review recent developments that have led from ILP to the broader field of MRDM. Briefly, these developments include the following: - the use of other declarative languages, including functional and higher-order languages, to represent data and learned knowledge [9, 6, 1]; - a better understanding of knowledge representation issues, and the importance of data modelling in MRDM tasks [7, 11]; - a better understanding of the relation between MRDM and standard single-table learning, and how to upgrade single-table methods to MRDM or downgrade MRDM tasks to single-table ones (propositionalisation) [3, 12, 10, 14]; - the study of non-classificatory learning tasks, such as subgroup discovery and multi-relational association rule mining [8, 4, 21]; - the incorporation of ROC analysis and cost-sensitive classification [20].