fgwcuv: Classical Fuzzy Geographicaly Weighted Clustering

In naspaclust: Nature-Inspired Spatial Clustering

Classical Fuzzy Geographicaly Weighted Clustering

Description

Fuzzy clustering with addition of spatial configuration of membership matrix

Usage

fgwcuv(
  data,
  pop,
  distmat,
  kind = NA,
  ncluster = 2,
  m = 2,
  distance = "euclidean",
  order = 2,
  alpha = 0.7,
  a = 1,
  b = 1,
  max.iter = 500,
  error = 1e-05,
  randomN = 0,
  uij = NA,
  vi = NA
)

Arguments

data	an object of data with d>1. Can be matrix or data.frame. If your data is univariate, bind it with 1 to get a 2 columns.
pop	an n*1 vector contains population.
distmat	an n*n distance matrix between regions.
kind	use 'u' if you want to use membership approach and 'v' for centroid approach.
ncluster	an integer. The number of clusters.
m	degree of fuzziness or fuzzifier. Default is 2.
distance	the distance metric between data and centroid, the default is euclidean, see cdist for details.
order	minkowski order. default is 2.
alpha	the old membership effect with [0,1], if alpha equals 1, it will be same as fuzzy C-Means, if 0, it equals to neighborhood effect.
a	spatial magnitude of distance. Default is 1.
b	spatial magnitude of population. Default is 1.
max.iter	maximum iteration. Default is 500.
error	error tolerance. Default is 1e-5.
randomN	random seed for initialisation (if uij or vi is NA). Default is 0.
uij	membership matrix initialisation.
vi	centroid matrix initialisation.

Details

Fuzzy Geographically Weighted Clustering (FGWC) was developed by \insertCitefgwc;textualnaspaclust by adding neighborhood effects and population to configure the membership matrix in Fuzzy C-Means. There are two kinds of options in doing classical FGWC. The first is using "u" \insertCiteRunkler2006naspaclust (default) for membership optimization and "v" \insertCitefgwcnaspaclust for centroid optimisation.

Value

an object of class "fgwc".
An "fgwc" object contains as follows:

• converg - the process convergence of objective function

• f_obj - objective function value

• membership - membership matrix

• centroid - centroid matrix

• validation - validation indices (there are partition coefficient (PC), classification entropy (CE), SC index (SC), separation index (SI), Xie and Beni's index (XB), IFV index (IFV), and Kwon index (Kwon))

• max.iter - Maximum iteration

• cluster - the cluster of the data

• finaldata - The final data (with the cluster)

• call - the syntax called previously

• time - computational time.

References

\insertAllCited

See Also

abcfgwc fpafgwc gsafgwc hhofgwc ifafgwc psofgwc tlbofgwc

Examples

data('census2010')
data('census2010dist')
data('census2010pop')
res1 <- fgwcuv(census2010,census2010pop,census2010dist,'u',3,2,'euclidean',4)

naspaclust documentation built on June 8, 2025, 1:51 p.m.