HPSCAN: Human Perception‐Based Scattered Data Clustering

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dc.contributor.authorHartwig, S.en_US
dc.contributor.authorOnzenoodt, C. v.en_US
dc.contributor.authorEngel, D.en_US
dc.contributor.authorHermosilla, P.en_US
dc.contributor.authorRopinski, T.en_US
dc.date.accessioned2025-03-07T16:48:54Z
dc.date.available2025-03-07T16:48:54Z
dc.date.issued2024
dc.description.abstractCluster separation is a task typically tackled by widely used clustering techniques, such as k‐means or DBSCAN. However, these algorithms are based on non‐perceptual metrics, and our experiments demonstrate that their output does not reflect human cluster perception. To bridge the gap between human cluster perception and machine‐computed clusters, we propose HPSCAN, a learning strategy that operates directly on scattered data. To learn perceptual cluster separation on such data, we crowdsourced the labeling of bivariate (scatterplot) datasets to 384 human participants. We train our HPSCAN model on these human‐annotated data. Instead of rendering these data as scatterplot images, we used their and point coordinates as input to a modified PointNet++ architecture, enabling direct inference on point clouds. In this work, we provide details on how we collected our dataset, report statistics of the resulting annotations, and investigate the perceptual agreement of cluster separation for real‐world data. We also report the training and evaluation protocol for HPSCAN and introduce a novel metric, that measures the accuracy between a clustering technique and a group of human annotators. We explore predicting point‐wise human agreement to detect ambiguities. Finally, we compare our approach to 10 established clustering techniques and demonstrate that HPSCAN is capable of generalizing to unseen and out‐of‐scope data.en_US
dc.description.number1
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.15275
dc.identifier.issn1467-8659
dc.identifier.pages15
dc.identifier.urihttps://doi.org/10.1111/cgf.15275
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15275
dc.publisherEurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcomputer vision—shape recognition
dc.subjectinformation visualization
dc.subjectmethods and applications
dc.subjectpoint‐based methods
dc.subjectvisualization
dc.subject• Human‐centered computing → Visualization theory, concepts and paradigms; Empirical studies in visualization; • Computing methodologies → Supervised learning
dc.titleHPSCAN: Human Perception‐Based Scattered Data Clusteringen_US
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