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ProSecCo: progressive sequence mining with convergence guarantees

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“Here growes the wine Pucinum, now called Prosecho, much celebrated by Pliny.”

–Fynes Moryson, An Itinerary, 1617

Abstract

We present ProSecCo, an algorithm for the progressive mining of frequent sequences from large transactional datasets: It processes the dataset in blocks and it outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences. ProSecCo can be used for interactive data exploration, as the intermediate results enable the user to make informed decisions as the computation proceeds. These intermediate results have strong probabilistic approximation guarantees and the final output is the exact collection of frequent sequences. Our correctness analysis uses the Vapnik–Chervonenkis (VC) dimension, a key concept from statistical learning theory. The results of our experimental evaluation of ProSecCo on real and artificial datasets show that it produces fast-converging high-quality results almost immediately. Its practical performance is even better than what is guaranteed by the theoretical analysis, and ProSecCo can even be faster than existing state-of-the-art non-progressive algorithms. Additionally, our experimental results show that ProSecCo uses a constant amount of memory, and orders of magnitude less than other standard, non-progressive, sequential pattern mining algorithms.

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Notes

  1. The last block may have fewer than \(b\) transactions.

  2. Some additional care is needed when handling the initial block. See Sect. 4.4.

  3. The last block may contain fewer than b transactions. For ease of presentation, we assume that all blocks have size \(b\).

  4. I.e., the ith intermediate result.

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Authors and Affiliations

  1. MIT CSAIL, Cambridge, MA, USA

    Sacha Servan-Schreiber & Emanuel Zgraggen

  2. Department of Computer Science, Amherst College, Amherst, MA, USA

    Matteo Riondato

Authors
  1. Sacha Servan-Schreiber
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  2. Matteo Riondato
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  3. Emanuel Zgraggen
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Correspondence to Matteo Riondato.

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A preliminary version of this work appeared in the proceedings of IEEE ICDM’18 [28], where it was deemed the runner-up for the Best Student Paper Award.

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Servan-Schreiber, S., Riondato, M. & Zgraggen, E. ProSecCo: progressive sequence mining with convergence guarantees. Knowl Inf Syst 62, 1313–1340 (2020). https://doi.org/10.1007/s10115-019-01393-8

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