maxp_greedy: A greedy algorithm to solve the max-p-region problem
In lixun910/rgeoda: R Library for Spatial Data Analysis

Description Usage Arguments Value Examples

The max-p-region problem is a special case of constrained clustering where a finite number of geographical areas, n, are aggregated into the maximum number of regions, p, such that each region satisfies the following const raints: 1. The areas within a region must be geographically connected.

maxp_greedy(
  w,
  data,
  bound_vals,
  min_bound,
  iterations = 99,
  initial_regions = vector("numeric"),
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6
)

`w`	An instance of Weight class
`data`	A list of numeric vectors of selected variable
`bound_vals`	A numeric vector of selected bounding variable
`min_bound`	A minimum value that the sum value of bounding variable int each cluster should be greater than
`iterations`	(optional): The number of iterations of greedy algorithm. Defaults to 99.
`initial_regions`	(optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
`distance_method`	(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
`random_seed`	(optional) The seed for random number generator. Defaults to 123456789.
`cpu_threads`	(optional) The number of cpu threads used for parallel computation

A list of numeric vectors represents a group of clusters

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
queen_w <- queen_weights(guerry)
guerry_df <- as.data.frame(guerry) # use as data.frame
data <- guerry_df[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
bound_vals <- guerry_df['Pop1831'][,1]
min_bound <- 3236.67 # 10% of Pop1831
maxp_clusters <- maxp_greedy(queen_w, data, bound_vals, min_bound, iterations=99)
maxp_clusters

## End(Not run)