Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ILP systems use such transformations, little is known about them or how they relate to each other. The paper describes a number of such transformations. Not all of them are novel, but there have been no studies comparing their efficacy. The main contributions of the paper are: (a) it clarifies the relationship between the transformations; (b) it contains an empirical study of what can be gained by applying the transformations; (c) it provides some guidance on the kinds of problems that are likely to benefit from the transformations.status: publishe
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficie...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the ecienc...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficie...
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or ...
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or ...
Introduction. This paper reports preliminary research in a primarily experimental study of how the g...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
Query optimization is used frequently in relational database management systems. Most existing techn...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
Learning query-transformation rules are vital for the success of semantic query optimization in doma...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficie...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the ecienc...
In Inductive Logic Programming (ILP), several techniques have been introduced to improve the efficie...
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or ...
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or ...
Introduction. This paper reports preliminary research in a primarily experimental study of how the g...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
Query optimization is used frequently in relational database management systems. Most existing techn...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
Learning query-transformation rules are vital for the success of semantic query optimization in doma...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...
An important motivation for the development of inductive databases and query languages for data mini...