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.

Colors encode the membership of elements 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.

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 corresponding regression plane share the same color.

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.