The paper proposes a new visual quality measure (VQM) called ClustML for quantifying the complexity of cluster patterns in scatterplots. ClustML is based on a Gaussian Mixture Model (GMM) that models the density of data points in the scatterplot.
The key novelty of ClustML is that it uses a data-driven approach to learn the merging decision function for pairs of GMM components, rather than relying on heuristics as in the previous ClustMe VQM. ClustML trains a binary classifier on human judgment data to predict whether two GMM components should be merged or not, capturing the perceptual complexity of the cluster patterns.
The paper demonstrates that ClustML outperforms ClustMe, the previous state-of-the-art GMM-based VQM, in terms of agreement with human judgments on two benchmark datasets. It also shows that ClustML can be used to analyze real-world data, such as in the domain of genome-wide association studies, to detect cluster patterns that may be missed by traditional approaches.
The paper also discusses the challenges in developing hybrid computational-perceptual VQMs for cluster patterns and argues for the importance of creating perceptual-study-based benchmark datasets for evaluating and designing new VQMs.
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by Most... pada arxiv.org 05-01-2024
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