Affiliations: Algorithmic Data Analysis Group, Department of
Information and Computing Sciences, Universiteit Utrecht, PO Box 80.089, 3508 TB
Utrecht, The Netherlands. [email protected]; [email protected]
Note: [] Address for correspondence: Algorithmic Data Analysis Group,
Department of Information and Computing Sciences, Universiteit Utrecht, PO Box
80.089, 3508 TB Utrecht, The Netherlands
Abstract: In recent years a variety of mining algorithms, to derive all
frequent subtrees from a database of labeled ordered rooted trees has been
developed. These algorithms share properties such as enumeration strategies and
pruning techniques. They differ however in the tree inclusion relation used and
the way attribute values are dealt with. In this work we investigate the
different approaches with respect to 'usefulness' of the
derived patterns, in particular, the performance of classifiers that use the
derived patterns as features. In order to find a good trade-off between
expressiveness and runtime performance of the different approaches, we also
take the complexity of the different classifiers into account, as well as the
run time and memory usage of the different approaches. The experiments are
performed on two real data sets, and two synthetic data sets. The results show
that significant improvement in both predictive performance and computational
efficiency can be gained by choosing the right tree mining approach.
