Visualization
Cybernetic Bloom
Show Cube

Regression Planes
Show Planes
Compute Intercept

Correlations
Show Correlations
Confidence Interval (%)

Cluster
Cluster Radius
Min Elements / Cluster

Axis Data
Price
Log
Volume
Log
Change
Log
H/L
Log
Trades
Log
Spread
Log


Cube Experiment

Identifies clusters in fast streaming data via a density-based spatial clustering algorithm. Regression planes visualize how each of the 3 dimensions is predicted by the other 2 dimensions.

Cluster

Colors encode the membership of elements (crypto USD markets) to a cluster, where elements of a cluster share the same color, except for gray elements that are not part of any cluster.

The most important parameter to control the size and quantity of clusters is the Cluster Radius: To generally find more small clusters, reduce it; to generally find fewer large clusters, increase it.

For the axis data, the use of log-transformation is recommended. If the same data is used for 2 axes, a 2D plane is created; if the same data is used for 3 axes, a 1D line is created.

Regression Planes

Regression planes visualize how each of the 3 dimensions is predicted by the other 2 dimensions. The axis label of the predicted dimension and the regression plane share the same color.