K Means¶
Group (Subgroup)¶
DREAM3D Review (Clustering)
Description¶
This Filter applies the k means algorithm to an Attribute Array. K means is a clustering algorithm that assigns to each point of the Attribute Array a cluster Id. The user must specify the number of clusters in which to partition the array. Specifically, a k means partitioning is a Voronoi tesselation; an optimal solution to the k means problem is such that each point in the data set is associated with the cluster that has the closest mean. This partitioning is the one that minimizes the within cluster variance (i.e., minimizes the within cluster sum of squares differences). Thus, the "metric" used for k means is the 2-norm (the Euclidean norm; the squared Euclidean norm may also be used since this maintains the triangle inequality).
Optimal solutions to the k means partitioning problem are computationally difficult; this Filter used Lloyd's algorithm to approximate the solution. Lloyd's algorithm is an iterative algorithm that proceeds as follows:
- Choose k points at random to serve as the initial cluster "means"
- Until convergence, repeat the following steps:
- Associate each point with the closest mean, where "closest" is the smallest 2-norm distance
- Recompute the means based on the new tesselation
Convergence is defined as when the computed means change very little (precisely, when the differences are within machine epsilon). Since Lloyd's algorithm is iterative, it only serves as an approximation, and may result in different classifications on each execution with the same input data. The user may opt to use a mask to ignore certain points; where the mask is false, the points will be placed in cluster 0.
A clustering algorithm can be considered a kind of segmentation; this implementation of k means does not rely on the Geometry on which the data lie, only the topology of the space that the array itself forms. Therefore, this Filter has the effect of creating either Features or Ensembles depending on the kind of array passed to it for clustering. If an Element array (e.g., voxel-level Cell data) is passed to the Filter, then Features are created (in the previous example, a Cell Feature Attribute Matrix will be created). If a Feature array is passed to the Filter, then an Ensemble Attribute Matrix is created. The following table shows what type of Attribute Matrix is created based on what sort of array is used for clustering:
| Attribute Matrix Source | Attribute Matrix Created |
|---|---|
| Generic | Generic |
| Vertex | Vertex Feature |
| Edge | Edge Feature |
| Face | Face Feature |
| Cell | Cell Feature |
| Vertex Feature | Vertex Ensemble |
| Edge Feature | Edge Ensemble |
| Face Feature | Face Ensemble |
| Cell Feature | Cell Ensemble |
| Vertex Ensemble | Vertex Ensemble |
| Edge Ensemble | Edge Ensemble |
| Face Ensemble | Face Ensemble |
| Cell Ensemble | Cell Ensemble |
This Filter will store the means for the final clusters within the created Attribute Matrix.
Parameters¶
| Name | Type | Description |
|---|---|---|
| Number of Clusters | int32_t | The number of clusters in which to partition the array |
| Distance Metric | Enumeration | The metric used to determine the distances between points; only 2-norm metrics (i.e., Euclidean or squared Euclidean) may be chosen |
| Use Mask | bool | Whether to use a boolean mask array to ignore certain points flagged as false from the algorithm |
Required Geometry¶
None
Required Objects¶
| Kind | Default Name | Type | Component Dimensions | Description |
|---|---|---|---|---|
| Any Attribute Array | None | Any | Any | The Attribute Array to cluster |
| Attrubute Array | Mask | bool | (1) | Specifies if the point is to be counted in the algorithm, if Use Mask is checked |
Created Objects¶
| Kind | Default Name | Type | Component Dimensions | Description |
|---|---|---|---|---|
| Attribute Matrix | ClusterData | Feature/Ensemble | N/A | The Attribute Matrix in which to store information associated with the created clusters |
| Attribute Array | ClusterIds | int32_t | (1) | Specifies to which cluster each point belongs |
| Attribute Array | ClusterMeans | double | (1) | The means of the final clusters |
References ##¶
[1] Least squares quantization in PCM, S.P. Lloyd, IEEE Transactions on Information Theory, vol. 28 (2), pp. 129-137, 1982.
Example Pipelines¶
License & Copyright¶
Please see the description file distributed with this plugin.
DREAM3D Mailing Lists¶
If you need more help with a filter, please consider asking your question on the DREAM3D Users mailing list: https://groups.google.com/forum/?hl=en#!forum/dream3d-users