Scalability and efficiency in multi-relational data mining
- 1 July 2003
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGKDD Explorations Newsletter
- Vol. 5 (1) , 17-30
- https://doi.org/10.1145/959242.959246
Abstract
Efficiency and Scalability have always been important concerns in the field of data mining, and are even more so in the multi-relational context, which is inherently more complex. The issue has been receiving an increasing amount of attention during the last few years, and quite a number of theoretical results, algorithms and implementations have been presented that explicitly aim at improving the efficiency and Scalability of multi-relational data mining approaches. With this article we attempt to present a structured overview.Keywords
This publication has 25 references indexed in Scilit:
- Beyond independence: probabilistic models for query approximation on binary transaction dataIEEE Transactions on Knowledge and Data Engineering, 2003
- Solving the multiple instance problem with axis-parallel rectanglesPublished by Elsevier ,1998
- Logical settings for concept-learningArtificial Intelligence, 1997
- On the relative expressiveness of description logics and predicate logicsArtificial Intelligence, 1996
- Phase transitions and the search problemArtificial Intelligence, 1996
- Tabled evaluation with delaying for general logic programsJournal of the ACM, 1996
- First-order jk-clausal theories are PAC-learnableArtificial Intelligence, 1994
- Inductive Logic Programming: Theory and methodsThe Journal of Logic Programming, 1994
- Query Optimization in Database SystemsACM Computing Surveys, 1984
- A Machine-Oriented Logic Based on the Resolution PrincipleJournal of the ACM, 1965