  Unlike conventional videos, 360{\deg} videos give freedom to users to turn
their heads, watch and interact with the content owing to its immersive
spherical environment. Although these movements are arbitrary, similarities can
be observed between viewport patterns of different users and different videos.
Identifying such patterns can assist both content and network providers to
enhance the 360{\deg} video streaming process, eventually increasing the
end-user Quality of Experience (QoE). But a study on how viewport patterns
display similarities across different video content, and their potential
applications has not yet been done. In this paper, we present a comprehensive
analysis of a dataset of 88 360{\deg} videos and propose a novel video
categorization algorithm that is based on similarities of viewports. First, we
propose a novel viewport clustering algorithm that outperforms the existing
algorithms in terms of clustering viewports with similar positioning and speed.
Next, we develop a novel and unique dynamic video segment categorization
algorithm that shows notable improvement in similarity for viewport
distributions within the clusters when compared to that of existing static
video categorizations.
