Efficient string mining under constraints via the deferred frequency index

Weese, David; Schulz, Marcel H.

doi:10.1007/978-3-540-70720-2_29

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Efficient string mining under constraints via the deferred frequency index

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Schulz, Marcel H.
Max Planck Society;

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Citation

Weese, D., & Schulz, M. H. (2008). Efficient string mining under constraints via the deferred frequency index. In P. Perner (Ed.), Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. Berlin/Heidelberg: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-7F86-8

Abstract

We propose a general approach for frequency based string mining, which has many applications, e.g. in contrast data mining. Our contribution is a novel algorithm based on a deferred data structure. Despite its simplicity, our approach is up to 4 times faster and uses about half the memory compared to the best-known algorithm of Fischer et al. Applications in various string domains, e.g. natural language, DNA or protein sequences, demonstrate the improvement of our algorithm.