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.