psofgwc: Fuzzy Geographicaly Weighted Clustering with Particle Swarm...

In naspaclust: Nature-Inspired Spatial Clustering

Fuzzy Geographicaly Weighted Clustering with Particle Swarm Optimization

Description

Fuzzy clustering with addition of spatial configuration of membership matrix with centroid optimization using Particle Swarm Algorithm.

Usage

psofgwc(
  data,
  pop = NA,
  distmat = NA,
  ncluster = 2,
  m = 2,
  distance = "euclidean",
  order = 2,
  alpha = 0.7,
  a = 1,
  b = 1,
  error = 1e-05,
  max.iter = 100,
  randomN = 0,
  vi.dist = "uniform",
  npar = 10,
  vmax = 0.7,
  pso.same = 10,
  c1 = 0.49,
  c2 = 0.49,
  w.inert = "sim.annealing",
  wmax = 0.9,
  wmin = 0.4,
  map = 0.4
)

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.
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.
error	error tolerance. Default is 1e-5.
max.iter	maximum iteration. Default is 500.
randomN	random seed for initialisation (if uij or vi is NA). Default is 0.
vi.dist	a string of centroid population distribution between 'uniform' (default) and 'normal'. Can be defined as vi.dist= in opt_param.
npar	number of particles. Can be defined as npar= in opt_param. Default is 10.
vmax	maximum velocity to be tolerated. Can be defined as vmax in opt_param. Default is 0.7
pso.same	number of consecutive unchange to stop the iteration. Can be defined as same= in opt_param.
c1	Cognitive scaling parameters. Can be defined as c1= in opt_param. Default is 0.49
c2	Social scaling parameters. Can be defined as c2= in opt_param. Default is 0.49,
w.inert	The inertia weight update method between "constant", "chaotic", "sim.annealing", "nat.exponent1", "nat.exponent2" based on Bansal (2011). Can be defined as type= in opt_param. Default is 'sim.annealing'
wmax	Maximum inertia weight. Can be defined as wmax in opt_param. Default is 0.9.
wmin	Minimum inertia weight. Can be defined as wmin in opt_param. Default is 0.4.
map	Chaotic mapping parameter. Userful when w.inert='chaotic'. Can be defined as map in opt_param. Default is 0.4.

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. Furthermore, the Particle Swarm Optimization was developed by \insertCiteKennedy1995;textualnaspaclust in order to get a more optimal solution of a certain complex function. PSO was also improved by \insertCiteBansal2011;textualnaspaclust by modifying the inertia weight. FGWC using PSO has been implemented previously by some studies \insertCitefgwcpso,putra2017naspaclust.

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

fpafgwc gsafgwc

Examples

data('census2010')
data('census2010dist')
data('census2010pop')
# First way
res1 <- psofgwc(census2010,census2010pop,census2010dist,3,2,'minkowski',4,npar=10)
# Second way
# initiate parameter
param_fgwc <- c(kind='v',ncluster=3,m=2,distance='minkowski',order=3,
               alpha=0.5,a=1.2,b=1.2,max.iter=1000,error=1e-6,randomN=10)
## tune the PSO parameter
pso_param <- c(vi.dist='uniform',npar=15,
         vmax=0.8, pso.same=10, c1=0.7, c2=0.6, type='chaotic',
                     wmax=0.8,wmin=0.3,map=0.3)
##FGWC with PSO
res2 <- fgwc(census2010,census2010pop,census2010dist,'pso',param_fgwc,pso_param)

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