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

Wolfgang Fuhl, Enkelejda Kasneci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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. Code can be downloaded here https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FHPCGen&mode=list.

Original languageEnglish
Title of host publicationProceedings - ETRA 2022
Subtitle of host publicationACM Symposium on Eye Tracking Research and Applications
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450392525
DOIs
StatePublished - 8 Jun 2022
Externally publishedYes
Event2022 ACM Symposium on Eye Tracking Research and Applications, ETRA 2022 - Virtual, Online, United States
Duration: 8 Jun 202211 Jun 2022

Publication series

NameEye Tracking Research and Applications Symposium (ETRA)

Conference

Conference2022 ACM Symposium on Eye Tracking Research and Applications, ETRA 2022
Country/TerritoryUnited States
CityVirtual, Online
Period8/06/2211/06/22

Keywords

  • Classification
  • Dynamic Stimulus
  • Eye Tracking
  • Fixed Stimulus
  • Gaze
  • Gaze Behaviour
  • Gaze Simulator
  • Gaze generation
  • Scan path
  • Scan path generation

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