Abstract
Groups of (parallel) point processes may be analyzed with a variety of different goals. Here we consider the case in which one has a special interest in finding subgroups of processes showing a behavior that differs significantly from the other processes. In particular, we are interested in finding subgroups that exhibit an increased synchrony. Finding such groups of processes poses a difficult problem as its naïve solution requires enumerating the power set of all processes involved, which is a costly procedure. In this paper we propose a method that allows us to efficiently filter the process set for candidate subgroups. We pay special attention to the possibilities of temporal imprecision, meaning that the synchrony is not exact, and selective participation, meaning that only a subset of the related processes participates in each synchronous event.
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Braune, C., Borgelt, C., Kruse, R. (2013). Behavioral Clustering for Point Processes. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_12
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DOI: https://doi.org/10.1007/978-3-642-41398-8_12
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