  In this paper, we present a new approach for decomposing scan paths and its
utility for generating new scan paths. For this purpose, we use the K-Means
clustering procedure to the raw gaze data and subsequently iteratively to find
more clusters in the found clusters. The found clusters are grouped for each
level in the hierarchy, and the most important principal components are
computed from the data contained in them. Using this tree hierarchy and the
principal components, new scan paths can be generated that match the human
behavior of the original data. We show that this generated data is very useful
for generating new data for scan path classification but can also be used to
generate fake scan paths.
