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In this paper, we present a new approach for decomposing scan paths and itsutility for generating new scan paths. For this purpose, we use the K-Meansclustering procedure to the raw gaze data and subsequently iteratively to findmore clusters in the found clusters. The found clusters are grouped for eachlevel in the hierarchy, and the most important principal components arecomputed from the data contained in them. Using this tree hierarchy and theprincipal components, new scan paths can be generated that match the humanbehavior of the original data. We show that this generated data is very usefulfor generating new data for scan path classification but can also be used togenerate fake scan paths.

HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration