[ ILPnet2 | Library | Newsletter | CSCW | Education | End-User Club | Events | Nodes | Systems | Applications | Members only ]
Discovery of Relational Association Rules
Luc Dehaspe
and Hannu Toivonen.
In Saso Dzeroski
and Nada Lavrac, editors, Relational Data Mining,
pages 189--212. Springer-Verlag, September 2001. More behind this link.
Abstract
Within KDD, the discovery of frequent patterns has been studied in a variety of
settings. In its simplest form, known from association rule mining, the task
is to discover all frequent item sets, i.e., all combinations of items that
are found in a sufficient number of examples. We present algorithms for
relational association rule discovery that are well-suited for exploratory
data mining. They offer the flexibility required to experiment with examples
more complex than feature vectors and patterns more complex than item sets.
BibTeX entry.
Other publications
L Dehaspe,
ldh@cs.kuleuven.ac.be. Last modified on Wednesday 9 April 2003 at 18:31. © 2003 ILPnet2