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Sky-signatures: detecting and characterizing recurrent behavior in sequential data

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Abstract

This paper proposes the sky-signature model, an extension of the signature model Gautrais et al. (in: Proceedings of the Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, 2017b) to multi-objective optimization. The signature approach considers a sequence of itemsets, and given a number k it returns a segmentation of the sequence in k segments such that the number of items occuring in all segments is maximized. The limitation of this approach is that it requires to manually set k, and thus fixes the temporal granularity at which the data is analyzed. The sky-signature model proposed in this paper removes this requirement, and allows to examine the results at multiple levels of granularity, while keeping a compact output. This paper also proposes efficient algorithms to mine sky-signatures, as well as an experimental validation both real data both from the retail domain and from natural language processing (political speeches).

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Notes

  1. http://people.irisa.fr/Thomas.Guyet/demo_signatures/signatures.php.

  2. http://www.presidency.ucsb.edu/2016_election.php.

  3. Including the 3 presidential debates Speeches of Clinton prior to April 2015 were discarded.

  4. Climate change issues became a topic of interest when Clinton attacked Trump on him saying that climate change is a hoax in the first presidential debate (September 26, 2016).

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Acknowledgements

We would like to thank the reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.

Funding

No funding was received to assist with the preparation of this manuscript. The initial work that lead to the first signature paper benefited from the funding of a 6-month internship in 2015 from the "Groupement des Mousquetaires" French retail group.

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Authors and Affiliations

  1. BrightClue, 2 bis Rue de la Châtaigneraie, 35510, Cesson-Sévigné, France

    Clément Gautrais

  2. INSA Rennes, CNRS, Inria, IRISA - UMR 6074, Univ Rennes, 35000, Rennes, France

    Peggy Cellier, René Quiniou & Alexandre Termier

  3. Inria, Lyon, France

    Thomas Guyet

Authors
  1. Clément Gautrais
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  2. Peggy Cellier
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  3. Thomas Guyet
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  4. René Quiniou
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  5. Alexandre Termier
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Correspondence to Clément Gautrais.

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Gautrais, C., Cellier, P., Guyet, T. et al. Sky-signatures: detecting and characterizing recurrent behavior in sequential data. Data Min Knowl Disc 38, 372–419 (2024). https://doi.org/10.1007/s10618-023-00949-1

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