Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
|Author||Surajit Ray and Yansong Cheng|
|Date of publication||2014-05-23 18:31:24|
|Maintainer||Surajit Ray <email@example.com>|
choose.cluster: Choosing the cluster which is closest to a specified point
contourHMAC: Plot clusters with different colors for two dimensional data...
cta20: Two dimensional data in original and log scale
disc2d: Two and three dimensional data representing two half discs
findmid: Find the mid point of memberships of each cluster
hard.HMAC: Plot clusters with different colors.
HMAC: Perform Modal Clustering in serial mode only
khat.inv: Calculate the smoothing paramters for implementation of Modal...
mydmvnorm: Calculate Density of Multivariate Normal for diagonal...
oned: One dimensional data with two main clusters
pHMAC: Main function for performing Modal Clusters either parallel...
plot.hmac: Plots of heierarchical tree for a 'hmac' object
soft.HMAC: Plot soft clusters from Modal Clustering output
summary.hmac: Summary of HMAC output