tlbofgwc: Fuzzy Geographicaly Weighted Clustering with Teaching -...

In naspaclust: Nature-Inspired Spatial Clustering

Fuzzy Geographicaly Weighted Clustering with Teaching - Learning Based Optimization

Description

Fuzzy clustering with addition of spatial configuration of membership matrix with centroid optimization using Teaching - Learning Based Algorithm.

Usage

tlbofgwc(
  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",
  nstud = 10,
  tlbo.same = 10,
  nselection = 10,
  elitism = F,
  n.elite = 2
)

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.
nstud	number of students. Can be defined as npar= in opt_param. Default is 10.
tlbo.same	number of consecutive unchange to stop the iteration. Can be defined as same= in opt_param. Default is 10.
nselection	number of teachers based on selected students. Can be defined as nselection= in opt_param. Default is equal to nstud.
elitism	wheter to use elitism algorithm or not. Either TRUE or FALSE. Can be defined as elitism= in opt_param. Default is FALSE.
n.elite	Number of elitist students. Can be defined as n.elite= in opt_param. Default is 2.

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 Teaching - Learning Based Optimization was developed by \insertCiteRao2012;textualnaspaclust and Developed by \insertCiteRao2012b;textualnaspaclust by adding the elitism algorithm in order to get a more optimal solution of a certain complex function.

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 <- tlbofgwc(census2010,census2010pop,census2010dist,3,2,'minkowski',4,nstud=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 TLBO parameter
tlbo_param <- c(vi.dist="uniform",nstud=10, tlbo.same=10,
         nselection=10,elitism=FALSE,n.elite=2)
##FGWC with TLBO
res2 <- fgwc(census2010,census2010pop,census2010dist,'tlbo',param_fgwc,tlbo_param)

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