A Multistrategy Approach to Relational Knowledge Discovery in Databases (1996)
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| Venue: | Machine Learning Journal |
| Citations: | 13 - 8 self |
BibTeX
@ARTICLE{Morik96amultistrategy,
author = {Katharina Morik and Peter Brockhausen},
title = {A Multistrategy Approach to Relational Knowledge Discovery in Databases},
journal = {Machine Learning Journal},
year = {1996},
volume = {27},
pages = {17--27}
}
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Abstract
. When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a model-driven way by t...
Keyphrases
multistrategy approach relational knowledge discovery hypothesis space algorithm detect hierarchy small data set opposite extreme model-driven way knowledge discovery inductive logic programming large database simple hypothesis language real-world database relational database management system ilp algorithm first-order logic complex learning algorithm
