CollectNNTransform
Collect nearest neighbors and append them to metadata.
 file: cluster/collectnn.cpp
 inherits: UntrainableMetaTransform
 author(s): Charles Otto

properties:
Property Type Description keep int The maximum number of nearest neighbors to keep. Default is 20.
KMeansTransform
Wraps OpenCV kmeans and flann.
 file: cluster/kmeans.cpp
 inherits: Transform
 author(s): Josh Klontz
 see:

properties:
Property Type Description kTrain int The number of random centroids to make at train time. Default is 256. kSearch int The number of nearest neighbors to search for at runtime. Default is 1.
KNNTransform
K nearest neighbors classifier.
 file: cluster/knn.cpp
 inherits: Transform
 author(s): Josh Klontz
 properties: None
LogNNTransform
Log nearest neighbors to specified file.
 file: cluster/lognn.cpp
 inherits: TimeVaryingTransform
 author(s): Charles Otto

properties:
Property Type Description fileName QString The name of the log file. An empty fileName won't be written to. Default is "".
MeanShiftClusteringTransform
A transform implementing the mean shift clustering algorithm.
 file: cluster/meanshift.cpp
 inherits: TimeVaryingTransform
 author(s): [Jordan Cheney][JordanCheney]
 see:

properties:
Property Type Description distance br::Distance* The distance used to compute the distance between templates kernelBandwidth int The size of the kernel used to converge points to a cluster center shiftThreshold float The cutoff threshold distance for a shifted point. A value lower then this threshold indicates a point has finished shifting to a cluster center. distanceThreshold float The distance threshold for a point to join a cluster. A point must be at least this close to another point to be included in the same cluster as that point.
OnlineRODTransform
Constructors clusters based on the RankOrder distance in an online, incremental manner
 file: cluster/onlinerod.cpp
 inherits: TimeVaryingTransform
 author(s): Charles Otto, [Jordan Cheney][JordanCheney]

read:
 Zhu et al.
"A RankOrder Distance based Clustering Algorithm for Face Tagging" CVPR 2011
 Zhu et al.

properties:
Property Type Description distance br::Distance* Distance to compute the similarity score between templates. Default is L2. kNN int Maximum number of nearest neighbors to keep for each template. Default is 20. aggression float Clustering aggresiveness. A higher value will result in larger clusters. Default is 10. incremental bool If true, compute the clusters as each template is processed otherwise compute the templates at the end. Default is false. evalOutput QString Path to store cluster informtation. Optional. Default is an empty string.
RandomCentroidsTransform
Chooses k random points to be centroids.
 file: cluster/randomcentroids.cpp
 inherits: Transform
 author(s): Austin Blanton
 see:

properties:
Property Type Description kTrain int The number of random centroids to make at train time. Default is 256. kSearch int The number of nearest neighbors to search for at runtime. Default is 1.