International Workshop on Constraint-Based Mining and Learning location:Warsaw, Poland date:21 September 2007
Constrained pattern mining extracts patterns based on
their individual merit. Usually this results in far more patterns than a human expert or a machine learning technique
could make use of. Often different patterns or combinations
of patterns cover a similar subset of the examples, thus being redundant and not carrying any new information. To remove the redundant information contained in such pattern
sets, we propose a general heuristic approach for selecting
a small subset of patterns.
We identify several selection techniques for use in this
general algorithm and evaluate those on several data sets.
The results show that the technique succeeds in severely reducing the number of patterns, while at the same time apparently retaining much of the original information. Additionally the experiments show that reducing the pattern set indeed improves the quality of classification results. Both results show that the approach is very well suited for the goals we aim at.