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.
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Haßler, M., Kohlschein, C., Meisen, T. (2018). Similarity Analysis of Time Interval Data Sets—A Graph Theory Approach. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Time Series Analysis and Forecasting. ITISE 2017. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-96944-2_11
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DOI: https://doi.org/10.1007/978-3-319-96944-2_11
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