Performing mode clustering and visualization.
- Paper reference: Chen, Yen-Chi, Christopher R. Genovese, and Larry Wasserman. "A comprehensive approach to mode clustering." Electronic Journal of Statistics 10.1 (2016): 210-241.
- Maintianer: [email protected]
EMC.R
: the main script for running the program.Example_OliveOil.R
: an example for applyingEMC
method to the Olive Oil dataset.
Here is a description for the main functions.
fms = function(data, query, h, eps=1.0e-8, max.iterations=100, cut = 0.1)
- Fast mean shift using heirachical clustering.
- Inputs:
- data: Input data matrix.
- query: The mesh points that you want to apply mean shift to.
- h: Smoothing parameter.
- max.iterations: Maximal number of iteration for mean shift.
- eps: The tolerance. If mean shift moves less than this value, we will consider it done.
- cut: The cut for heirachical clustering (we cut the dedrogram by height = cut*h).
- Outputs:
- The mode clustering result; a list consisting of
- label: The cluster labels for query points.
- modes: The local modes corresponding to each label.
- The mode clustering result; a list consisting of
EMC = function(data, h=NULL, eps=1.0e-8, max.iterations=100, ...) UseMethod("EMC")
EMC.default = function(data, h=NULL, eps=1.0e-8, max.iterations=100, n0= NULL, rho=NULL, cut=0.1, noisy = F, T_denoise =5)
- Enhanced mode clustering.
- Inputs:
- data: Input data matrix.
- h: Smoothing parameter.
- max.iterations: Maximal number of iteration for mean shift.
- eps: The tolerance. If mean shift moves less than this value, we will consider it done.
- n0: The thresholding size for tiny clusters. Default is to use the method given in Chen et al. (2014).
- rho: The contrast parameter for visualization. Default is to use the method given in Chen et al. (2014).
- noisy: True or False. To desplay noisy clusters (without thresholding). Default is False.
- T_denoise: Maximal number of denoising. If tiny clusters presence, we will remove them and redo mode clustering. This is the maximal number of redoing mean shift clustering.
- cut: The cut for heirachical clustering (we cut the dedrogram by height = cut*h).
- Output:
- An S4 object about summary informations using enhanced mode clustering. A list consisting:
- label: The cluster labels for query points.
- modes: The local modes corresponding to each label.
- c.matrix: The connectivity matrix.
- vis.data: The visualization coordinates for data points.
- vis.modes: The visualization coordinates for local modes.
- SC.plot: The size of ordered clusters before denoising.
- size.threshold: The size threshold for denoising tiny clusters.
- bandwidth: The smoothing bandwidth.
- rho: The contrast paramter used for visualization.
- noisy.label: The cluster labels for query points before denoising.
- noisy.modes: The local modes corresponding to each label before denoising.
- An S4 object about summary informations using enhanced mode clustering. A list consisting: