Scalable sequential pattern mining for biological sequences

  title={Scalable sequential pattern mining for biological sequences},
  author={Ke Wang and Yabo Xu and Jeffrey Xu Yu},
Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., "don't care") of arbitrary size. Mining frequent patterns in such sequences faces a different type of explosion than in transaction sequences primarily motivated in market-basket analysis. In this paper, we study how this explosion affects the classic sequential pattern mining, and present a scalable two-phase algorithm to deal with this new explosion. The <i>Segment Phase</i> first searches for… CONTINUE READING
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