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Pattern research in the digital humanities: how data mining techniques support the identification of costume patterns

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Computer Science - Research and Development

Abstract

Costumes are prominent in transporting a character’s mood, a certain stereotype, or character trait in a film. The concept of patterns, applied to the domain of costumes in films, can help costume designers to improve their work by capturing knowledge and experience about proven solutions for recurring design problems. However, finding such Costume Patterns is a difficult and time-consuming task, because possibly hundreds of different costumes of a huge number of films have to be analyzed to find commonalities. In this paper, we present a Semi-Automated Costume Pattern Mining Method to discover indicators for Costume Patterns from a large data set of documented costumes using data mining and data warehouse techniques. We validate the presented approach by a prototypical implementation that builds upon the Apriori algorithm for mining association rules and standard data warehouse technologies.

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Notes

  1. As part of the MUSE project (last accessed on 25.02.2016): http://www.iaas.uni-stuttgart.de/forschung/projects/MUSE.

  2. As the term pattern is ambigious and used besides the costume domain also in the domain of data mining (cf. [8]) we clarify the different meanings at this point. While data mining is utilized to find patterns in large data sets in the form of similarities, relations, and rules, costume patterns follow the principles of the pattern approach by Alexander et al. [2].

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Authors and Affiliations

  1. Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany

    Michael Falkenthal, Johanna Barzen, Uwe Breitenbücher & Frank Leymann

  2. Herman Hollerith Zentrum, University of Applied Sciences Reutlingen, Reutlingen, Germany

    Sascha Brügmann, Daniel Joos & Michael Wurster

Authors
  1. Michael Falkenthal
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  2. Johanna Barzen
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  3. Uwe Breitenbücher
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  4. Sascha Brügmann
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  5. Daniel Joos
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  6. Frank Leymann
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  7. Michael Wurster
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Correspondence to Michael Falkenthal.

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Falkenthal, M., Barzen, J., Breitenbücher, U. et al. Pattern research in the digital humanities: how data mining techniques support the identification of costume patterns. Comput Sci Res Dev 32, 311–321 (2017). https://doi.org/10.1007/s00450-016-0331-6

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