This is a low-level node that can output either points with a cluster attribute or cluster centers. For a higher-level clustering node that can do both at the same time, see the Cluster points node.

This node creates a cluster integer attribute on the points which indicates the cluster number the point belongs to (all points in the same cluster will have the same cluster value).

This node uses the K-means clustering algorithm to group the points.

The K-means clustering algorithm used by this node is sensitive to the randomly chosen initial cluster centers, especially when you create a small number of clusters. For example, if you have geometry divided into two parts that are far from each other, you might end up with all the points of part A in one cluster, and the points of part B divided into many clusters, just because none of the initial centers happened to be near part A.

In this case you can change the Seed parameter until you get a better clustering.

This node lets you cluster by blending multiple attributes (by increasing the Control attributes parameter). Each attribute has a Weight parameter controlling its linear contribution to the proximity measurement that controls which cluster each point goes into.

For some attributes, you may find that a linear scale is not what you want. The example that motivated the threshold feature is clustering points by impact time. For example:

• You use an Impact Analysis DOP to create points where RBD objects hit the ground, in order to create dust clouds. The impact points have an impacttime attribute containing a time stamp of when the impact occurred.

• You want to cluster the impact points to put them in separate fluid simulation boxes.

• You want to sort by location (so points close to each other are in the same box), but also by impact time (so impact points that occur far apart from each other in time are in separate boxes, even if they occur at the same place).

• You could use two “control attributes”, position (P) and impacttime, to control clustering, with a higher Weight value for impacttime to group the points by impact time, then by position.

• However, the linear nature of the Weight parameter may make it difficult to pick the right weight value to get the behavior you want, where impacts that are “close enough” in time go into the same box, but otherwise are clustered separately.

This is what the threshold feature is useful for. Instead of a linear weight, it adds a high “penalty” to the final distance calculation if the distance of a certain attribute is more than a certain amount (the threshold).

In this example, the red points represent early impacts, and the green points represent later impacts. You can see that in some places points impacts that overlap in position are sorted into different boxes based on different impact times.