One in a million: picking the right patterns |
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Authors: | Björn Bringmann Albrecht Zimmermann |
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Affiliation: | (1) Departement Computerwetenschappen, Katholieke Universiteit Leuven, Celestijnenlaan 200a, 3001 Heverlee, Belgium |
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Abstract: | Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than
a human expert or a machine leaning 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 two general heuristic algorithms—Bouncer and Picker—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 both techniques succeed 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 developed solutions are very well suited for the goals we
aim at.
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Keywords: | Data mining Post processing Pattern reduction |
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