Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Inductive characterisation of database relations
Methodologies for intelligent systems, 5
The design of relational databases
The design of relational databases
Learning by discovering concept hierarchies
Artificial Intelligence
On the menbership problem for functional and multivalued dependencies in relational databases
ACM Transactions on Database Systems (TODS)
Machine Learning
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Predicate Invention in Inductive Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
Theory of Relational Databases
Theory of Relational Databases
Database dependency discovery: a machine learning approach
AI Communications
A note on approximation measures for multi-valued dependencies in relational databases
Information Processing Letters
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Information-theoretic tools for mining database structure from large data sets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Functional and multivalued dependencies in nested databases generated by record and list constructor
Annals of Mathematics and Artificial Intelligence
Discovering data quality rules
Proceedings of the VLDB Endowment
Information Sciences: an International Journal
Inductive logic programming in databases: From datalog to $\mathcal{dl}+log}^{\neg\vee}$
Theory and Practice of Logic Programming
Border algorithms for computing hasse diagrams of arbitrary lattices
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
The problem of finding the sparsest bayesian network for an input data set is NP-Hard
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
| Hi-index | 0.00 |
Discovery of multivalued dependencies from database relations is viewed as a search in a hypothesis space defined according to the generalisation relationship among multivalued dependencies. Two algorithms for the discovery of multivalued dependencies from relations are presented. The top-down algorithm enumerates the hypotheses from the most general to more specific hypotheses which are checked on the input relation. The bottom-up algorithm first computes the invalid multivalued dependencies. Starting with the most general dependencies, the algorithm iteratively refines the set of dependencies to conform with each particular invalid dependency. The implementation of the algorithms is analysed and some empirical results are presented.