abcfgwc: Fuzzy Geographicaly Weighted Clustering with Artificial Bee...

In naspaclust: Nature-Inspired Spatial Clustering

Fuzzy Geographicaly Weighted Clustering with Artificial Bee Colony Optimization

Description

Fuzzy clustering with addition of spatial configuration of membership matrix with centroid optimization using Artificial Bee Colony

Usage

abcfgwc(
  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",
  nfood = 10,
  n.onlooker = 5,
  limit = 4,
  pso = F,
  abc.same = 10
)

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.
nfood	number of foods population. Can be defined as npar= in opt_param.
n.onlooker	number of onlooker bees, Can be defined as n.onlooker in opt_param.
limit	number of turns to eliminate food with no solutions. Can be defined as limit in opt_param.
pso	whether to add PSO term in bee's movement. Either TRUE or FALSE. Can be defined as pso in opt_param.
abc.same	number of consecutive unchange to stop the iteration. Can be defined as same= in opt_param.

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 Artificial Bee Colony (ABC) algorithm was developed by \insertCiteKaraboga2007;textualnaspaclust in order to get a more optimal solution of a certain complex function. FGWC using ABC has been implemented previously by \insertCitefgwcabc1;textualnaspaclust and \insertCitefgwcabc2;textualnaspaclust.

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 <- abcfgwc(census2010,census2010pop,census2010dist,3,2,'euclidean',4,nfood=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 ABC parameter
abc_param <- c(vi.dist='normal',npar=5,pso=FALSE,same=15,n.onlooker=5,limit=5) 
##FGWC with ABC optimization algorithm
res2 <- fgwc(census2010,census2010pop,census2010dist,'abc',param_fgwc,abc_param) 

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