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Similarity Analysis of Time Interval Data Sets—A Graph Theory Approach

  • Marc HaßlerEmail author
  • Christian Kohlschein
  • Tobias MeisenEmail author
Conference paper
  • 1.1k Downloads
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Comparison of entities, i.e., the measurement of their similarity, is a frequent, but challenging task in computer science. It requires a precise and quantifiable definition of similarity itself. Are two texts equal, if they overlap in a majority of their composing words? Does a pair of pictures resemble the same content? What defines the sameness of two songs? While certain distance-based approaches, e.g., Minkowski, make for a good starting point in defining similarity, there is no one-size-fits-all approach. In this work, we tackle a particularly interesting problem, namely, the definition of a similarity measure for comparing time interval data sets. Our approach regards the data sets as disjoint parts of a bigraph, thereby allowing for an application of methods from graph theory. We present both a formal definition of the similarity of two time intervals and our methods as well as concrete use-case from the medical domain, thus demonstrating the applicability for real-world scenarios.

Keywords

Graph theory Time interval data set Similarity analysis Medical data analysis Distance measures 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Information Management in Mechanical Engineering, RWTH Aachen UniversityAachenGermany

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