Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12]

▼Nkmeans	Namespace for k-means clustering
CConsecutiveAccessExtractor	Extractor for accessing consecutive observations
CDetails	Additional statistics from the k-means algorithm
CIndexedAccessExtractor	Extractor for accessing indexed observations
CInitialize	Interface for k-means initialization algorithms
CInitializeKmeanspp	k-means++ initialization of Arthur and Vassilvitskii (2007)
CInitializeKmeansppOptions	Options for k-means++ initialization
CInitializeNone	No-op "initialization" with existing cluster centers
CInitializeRandom	Initialize by sampling random observations without replacement
CInitializeRandomOptions	Options to use for InitializeRandom
CInitializeVariancePartition	Implements the variance partitioning method of Su and Dy (2007)
CInitializeVariancePartitionOptions	Options for InitializeVariancePartition
CMatrix	Interface for matrix data
CRandomAccessExtractor	Extractor for accessing random observations
CRefine	Interface for k-means refinement algorithms
CRefineHartiganWong	Implements the Hartigan-Wong algorithm for k-means clustering
CRefineHartiganWongOptions	Options for RefineHartiganWong
CRefineLloyd	Implements the Lloyd algorithm for k-means clustering
CRefineLloydOptions	Options for RefineLloyd construction
CRefineMiniBatch	Implements the mini-batch algorithm for k-means clustering
CRefineMiniBatchOptions	Options for RefineMiniBatch construction
CResults	Full statistics from k-means clustering
CSimpleMatrix	A simple matrix of observations