The Cluster operator allows you to partition particles into groups and then save the resulting group index to a custom data channel, for later use in other operators.
Voronoi cells: cluster groups will take the shape of voronoi cells, derived from nearest-point proximity groupings.
Turbulent noise: cluster groups will be derived from 3D turbulent noise values.
LDNP: cluster groups will be derived from LDNPs (local distributions of neighboring particles). This is an approximated k-means clustering method.
Max unique: the maximum number of cluster groups.
Bias: the amount of bias that group indices will have towards lower values.
Channel: the custom data float channel to save the resulting group indices to.
Show cluster points: show the individual cluster points (voronoi cell centers) in the viewport.
Visual clusters: display each particle as a random color, based on their cluster group index.
Particle cloud center: the center of the point cloud will be located at the center of the particle cloud.
Object pivots: point clouds will be located at the pivot points of input objects.
Count: the number of points in the point cloud.
Fuzzing: the amount of implicit jitter to add to each particle prior to point-cloud proximity searches, which will blur the borders between the resulting cells.
Sphere/Box: the shape of the point cloud bounds.
Scale X/Y/Z: the scale of each point cloud axis.
Scale mult: the overall scale multiplier for the point cloud.
Scale: the scale of the turbulent noise.
Min level: clamps the minimum value of the resulting noise values.
Max level: clamps the maximum level of the resulting noise values.
Iterations: the number of iterations of turbulent noise to compute. Higher iterations will result in more detailed noise.
Fuzzing: the amount of implicit jitter to add to each particle prior to noise calculations, which will blur the borders between the resulting shapes.
Radius: the radius of the particle neighborhoods.