MonoClust: Monothetic Clustering
In monoClust: Perform Monothetic Clustering with Extensions to Circular Data

Description Usage Arguments Value References Examples

Creates a MonoClust object after partitioning the data set using Monothetic Clustering.

MonoClust(
  toclust,
  cir.var = NULL,
  variables = NULL,
  distmethod = NULL,
  digits = getOption("digits"),
  nclusters = 2L,
  minsplit = 5L,
  minbucket = round(minsplit/3),
  ncores = 1L
)

`toclust`	Data set as a data frame.
`cir.var`	Index or name of the circular variable in the data set.
`variables`	List of variables selected for clustering procedure. It could be a vector of variable indexes, or a vector of variable names.
`distmethod`	Distance method to use with the data set. Can be chosen from "euclidean" (for Euclidean distance), "mahattan" (for Manhattan distance), or "gower" (for Gower distance). If not set, Euclidean distance is used unless `cir.var` is set, then it is Gower distance is used by default. Abbreviations can be used.
`digits`	Significant decimal number printed in the output.
`nclusters`	Number of clusters created. Default is 2.
`minsplit`	The minimum number of observations that must exist in a node in order for a split to be attempted. Default is 5.
`minbucket`	The minimum number of observations in any terminal leaf node. Default is `minsplit/3`.
`ncores`	Number of CPU cores on the current host. If greater than 1, parallel processing with `foreach::foreach()` is used to distribute cut search on variables to processes. When set to NULL, all available cores are used.

A MonoClust object. See MonoClust.object.

Chavent, M. (1998). A monothetic clustering method. Pattern Recognition Letters, 19(11), 989-996. doi: 10.1016/S0167-8655(98)00087-7.
Tran, T. V. (2019). Monothetic Cluster Analysis with Extensions to Circular and Functional Data. Montana State University - Bozeman.

# Very simple data set
library(cluster)
data(ruspini)
ruspini4sol <- MonoClust(ruspini, nclusters = 4)
ruspini4sol

# data with circular variable
library(monoClust)
data(wind_sensit_2007)

# Use a small data set
set.seed(12345)
wind_reduced <- wind_sensit_2007[sample.int(nrow(wind_sensit_2007), 10), ]
circular_wind <- MonoClust(wind_reduced, cir.var = 3, nclusters = 2)
circular_wind