Clustering Paths With Dynamic Time Warping
Rainer Koschke, Marcel Steinbeck · 2020
Studying software visualization often includes the evaluation of paths collected from participants of a study (e.g., eye tracking or movements in virtual worlds). In this paper, we explore clustering techniques to automate the process of grouping similar paths. The heart of the evaluated approach is a distance metric between paths that is based on dynamic time warping (DTW). DTW aligns two paths based on any given distance metric between their data points so as to minimize the distance between those paths-alignment may stretch or compress time for best fit. With a data set of 127 paths of professional software developers exploring code cities in virtual reality, we evaluate the clustering based on objective quality indices and manual inspection.