Canonical Patterns of Oriented Topologies

A common problem in many areas of behavioral research is the analysis of the large volume of data recorded during the execution of the tasks being studied. Recent work has proposed the use of an automated method based on canonical sets to identify the most representative patterns in a large data set, and described an initial experiment in identifying canonical web-browsing patterns. However, there is a significant limitation to the method: it requires the similarity matrix to be symmetric, and thus can only be used for problems that can be modeled as unoriented topologies. In this paper we propose a novel enhancement to the method to support oriented topologies by allowing the similarity matrix to be nonsymmetric. We demonstrate the power of this new technique by applying the new method to find canonical lane changes in a driving simulator experiment.

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