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Interactive Interpretation of Serial Episodes: Experiments in Musical Analysis

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Knowledge Engineering and Knowledge Management (EKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11313))

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Abstract

We propose an interactive approach for post-processing serial episodes mined from sequential data, i.e. time-stamped sequences of events. The strength of the approach rests upon an interactive interpretation that relies on a web interface featuring various tools for observing, sorting and filtering the mined episodes. Features of the approach include interestingness measures, interactive visualization of episode occurrences in the mined event sequence, and an automatic filtering mechanism that remove episodes depending on the analyst’s previous actions. We report experiments that show the advantages and limits of this approach in the domain of melodic analysis.

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Notes

  1. 1.

    http://liris.cnrs.fr/~crigotti/dmt4sp.html.

  2. 2.

    Event types are represented with integer values.

  3. 3.

    Here, the notes have 4, 3 and 1 beats, and the corresponding note values are respectively whole note, dotted half note and quarter note.

  4. 4.

    The computation of the closure property in Transmute is based on the number of occurrences of a serial episode. It is not detailed in this paper.

References

  1. Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge discovery in databases: an overview. AI Mag. 13(3), 57–70 (1992)

    Google Scholar 

  2. Holzinger, A.: Human-computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40511-2_22

    Chapter  Google Scholar 

  3. van Leeuwen, M.: Interactive data exploration using pattern mining. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. LNCS, vol. 8401, pp. 169–182. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43968-5_9

    Chapter  Google Scholar 

  4. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  Google Scholar 

  5. Vreeken, J., Leeuwen, M., Siebes, A.: KRIMP: mining itemsets that compress. Data Mining Knowl. Disc. 23(1), 169–214 (2011)

    Article  MathSciNet  Google Scholar 

  6. Lam, H.T., Mörchen, F., Fradkin, D., Calders, T.: Mining compressing sequential patterns. Stat. Anal. Data Mining 7(1), 34–52 (2014)

    Article  MathSciNet  Google Scholar 

  7. Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, pp. 12–20. ACM (2009)

    Google Scholar 

  8. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: IEEE Symposium on Visual Languages. Proceedings, pp. 336–343. IEEE (1996)

    Google Scholar 

  9. Gotz, D., Wang, F., Perer, A.: A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. J. Biomed. Inform. 48, 148–159 (2014)

    Article  Google Scholar 

  10. Stahl, F., Gabrys, B., Gaber, M.M., Berendsen, M.: An overview of interactive visual data mining techniques for knowledge discovery. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(4), 239–256 (2013)

    Google Scholar 

  11. Dzyuba, V., Van Leeuwen, M., Nijssen, S., De Raedt, L.: Active preference learning for ranking patterns. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 532–539. IEEE (2013)

    Google Scholar 

  12. Bie, T.: Subjective interestingness in exploratory data mining. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 19–31. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41398-8_3

    Chapter  Google Scholar 

  13. Boley, M., Mampaey, M., Kang, B., Tokmakov, P., Wrobel, S.: One click mining: interactive local pattern discovery through implicit preference and performance learning. In: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, pp. 27–35. ACM (2013)

    Google Scholar 

  14. Tatti, N.: Discovering episodes with compact minimal windows. Data Min. Knowl. Discov. 28(4), 1046–1077 (2014)

    Article  MathSciNet  Google Scholar 

  15. Nanni, M., Rigotti, C.: Extracting trees of quantitative serial episodes. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 170–188. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75549-4_11

    Chapter  Google Scholar 

  16. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 259–289 (1997)

    Google Scholar 

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Authors and Affiliations

  1. Université de Lyon, UJML3, IAE, LIRIS, 69 008, Lyon, France

    Béatrice Fuchs

  2. Université de Lyon, LIRIS, 69 100, Villeurbanne, France

    Amélie Cordier

Authors
  1. Béatrice Fuchs
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  2. Amélie Cordier
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Corresponding author

Correspondence to Béatrice Fuchs .

Editor information

Editors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Catherine Faron Zucker

  2. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  3. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Amedeo Napoli

  4. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Yannick Toussaint

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Fuchs, B., Cordier, A. (2018). Interactive Interpretation of Serial Episodes: Experiments in Musical Analysis. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-03667-6_9

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  • Online ISBN: 978-3-030-03667-6

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