This illustrates a variety of example data structures that can be generated. These synthetic datasets represent common and challenging shapes found.
A simple spherical Gaussian cluster with very small variance in \(4\text{-}D\) space.
Uniform points in a hypercube with a hollow center, useful for detecting voids or gaps.
A curvilinear loop structure that forms a closed cycle in \(4\text{-}D\).
A tree-like curvilinear structure with six branches in \(6\text{-}D\), ideal for studying bifurcation patterns.
A pointed cone shape in \(4\text{-}D\), controlled by height and radius ratio.
A spiral winding around a conical surface in \(5\text{-}D\) space.
A regularly spaced sphere with high point density, useful for manifold learning.
An S-shaped manifold in \(8\text{-}D\) with a missing section, to evaluate resilience to structural gaps.
A \(4\text{-}D\) pyramid-shaped structure with a void at the center.
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