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Ariella Richardson, Gal A. Kaminka, and Sarit Kraus. REEF: Resolving Length Bias in Frequency Sequence Mining. In The Third International Conference on Advances in Information Mining and Management (IMMM-2013), 2013. Winner: Best paper award.
Classic support based approaches efficiently address frequent sequence mining. However, support based mining has been shown to suffer from a bias towards short sequences. In this paper, we propose a method to resolve this bias when mining the most frequent sequences. In order to resolve the length bias we define norm-frequency, based on the statistical z-score of support, and use it to replace support based frequency. Our approach mines the subsequences that are frequent relative to other subsequences of the same length. Unfortunately, naive use of norm-frequency hinders mining scalability. Using norm-frequency breaks the anti-monotonic property of support, an important part in being able to prune large sets of candidate sequences. We describe a bound that enables pruning to provide scalability. Experimental results on textual and computer user input data establish that we manage to overcome the short sequence bias successfully, and to illustrate the production of meaningful sequences with our mining algorithm.
@InProceedings{immm13,
author = {Ariella Richardson and Gal A. Kaminka and Sarit Kraus},
title = {{REEF}: Resolving Length Bias in Frequency Sequence Mining},
booktitle = {The Third International Conference on Advances in Information Mining and Management ({IMMM}-2013)},
year = {2013},
abstract = {Classic support based approaches efficiently address frequent sequence mining.
However, support based mining has been shown to suffer from a bias towards short sequences.
In this paper, we propose a method to resolve this bias when mining the most frequent
sequences. In order to resolve the length bias we define norm-frequency, based on the
statistical z-score of support, and use it to replace support based frequency.
Our approach mines the subsequences that are frequent relative
to other subsequences of the same length. Unfortunately, naive use of norm-frequency
hinders mining scalability. Using norm-frequency breaks the anti-monotonic property of support,
an important part in being able to prune large sets of candidate sequences.
We describe a bound that enables pruning to provide
scalability. Experimental results on textual and computer user
input data establish that we manage to overcome the short
sequence bias successfully, and to illustrate the production of
meaningful sequences with our mining algorithm.},
note = {{\bf Winner: Best paper award.}},
}
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