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

arXiv: https://arxiv.org/abs/2201.08354 · HTML: https://mlsanity.com/paper/2201.08354/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity](https://mlsanity.com/paper/2412.04184/) (2024)
- [Area of interest adaption using feature importance](https://mlsanity.com/paper/2303.12744/) (2023)
- [Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets](https://mlsanity.com/paper/2206.09697/) (2022)
- [A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification](https://mlsanity.com/paper/2303.12757/) (2023)
- [EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis](https://mlsanity.com/paper/2609.04501/) (2026)

## Abstract (arXiv)

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.