Log In Sign Up

Clusterplot: High-dimensional Cluster Visualization

by   Or Malkai, et al.

We present Clusterplot, a multi-class high-dimensional data visualization tool designed to visualize cluster-level information offering an intuitive understanding of the cluster inter-relations. Our unique plots leverage 2D blobs devised to convey the geometrical and topological characteristics of clusters within the high-dimensional data, and their pairwise relations, such that general inter-cluster behavior is easily interpretable in the plot. Class identity supervision is utilized to drive the measuring of relations among clusters in high-dimension, particularly, proximity and overlap, which are then reflected spatially through the 2D blobs. We demonstrate the strength of our clusterplots and their ability to deliver a clear and intuitive informative exploration experience for high-dimensional clusters characterized by complex structure and significant overlap.


page 3

page 4

page 5

page 6

page 7

page 10

page 13

page 14


Extending Scatterplots to Scalar Fields

Embedding high-dimensional data into a 2D canvas is a popular strategy f...

Unsupervised Discovery of Sparse Multimodal Representations in High Dimensional Data

Extracting an understanding of the underlying system from high dimension...

Subspace Shapes: Enhancing High-Dimensional Subspace Structures via Ambient Occlusion Shading

We test the hypothesis whether transforming a data matrix into a 3D shad...

Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections

We propose Steadiness and Cohesiveness, two novel metrics to measure the...