| Type: | Package |
| Title: | Fuzzy Adaptive Resonance Theory K-Means Clustering Technique |
| Version: | 0.1.0 |
| Author: | Alfi Nurrahmah [aut, cre] Budi Yuniarto [aut] |
| Maintainer: | Alfi Nurrahmah <221810140@stis.ac.id> |
| Description: | A set of function for clustering data observation with hybrid method Fuzzy ART and K-Means by Sengupta, Ghosh & Dan (2011) <doi:10.1080/0951192X.2011.602362>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.2 |
| URL: | <https://github.com/alfinurrahmah/fakmct> |
| Imports: | stats |
| Depends: | R (≥ 3.5.0) |
| NeedsCompilation: | no |
| Packaged: | 2022-06-21 08:50:03 UTC; ALFI |
| Repository: | CRAN |
| Date/Publication: | 2022-06-22 19:20:05 UTC |
Choice Function
Description
Calculates the similarity between the input pattern I and all of saved categories.
Usage
choice_function(input, category_w, alpha)
Arguments
input |
The input (vector) data observation |
category_w |
The current category weight |
alpha |
Choice parameter alpha > 0 |
Value
Returns the vector of Tj choice activation function
Fuzzy Adaptive Resonance Theory (ART) K-Means Clustering Technique
Description
Clustering data observation with hybrid method Fuzzy ART and K-Means
Usage
fakmct(
input,
rho,
alpha,
beta,
w_init = NA,
max_epochs = 1000,
max_clusters = 1000,
eps = 10^-6
)
Arguments
input |
The input (vector) data observation. Should be numeric type of data. |
rho |
Vigilance parameter in (0,1) |
alpha |
Choice parameter alpha > 0 |
beta |
Learning rate in (0,1) |
w_init |
Initial weight |
max_epochs |
Maximum number of iterations |
max_clusters |
Maximum number of clusters that allowed |
eps |
Tolerance with default is 10^-6 |
Value
labels |
clusters label of each observations |
size |
the size of each clusters that have been formed |
clusters |
a list of observations in each clusters |
centroids |
cluster centroids that are calculated by the mean value of the objects in each clusters |
weights |
the model weight |
params |
parameters that have been saved in the function |
num_clusters |
number of cluster that have been formed |
running.time |
time for running function |
Examples
library(fakmct)
# Using dataset iris
## load data
data.inputs = iris[,-5]
true.labels = as.numeric(unlist(iris$Species))
## run model data
ex.iris<-fakmct(data.inputs, alpha = 0.3, rho = 0.5, beta = 1, max_epochs = 50, max_clusters = 5)
ex.iris$labels
ex.iris$size
ex.iris$centroids
ex.iris$params
## plot data
plot(data.inputs, col = ex.iris$labels, pch = true.labels,
main = paste0("Dataset: Iris"))
# Using data IPM 2019
## load simulate data IPM
data("simulatedataIPM")
dt <- simulatedataIPM
## run model data IPM
mod.fakm<-fakmct(dt, alpha = 0.3, rho = 0.5, beta = 0.1, max_epochs = 50, max_clusters = 5)
mod.fakm$labels
mod.fakm$size
mod.fakm$centroids
mod.fakm$params
## plot data IPM
plot(dt, col = mod.fakm$labels, pch=mod.fakm$labels, main = paste0("Dataset IPM"))
Fuzzy And Function
Description
Fuzzy And Function
Usage
fuzzy_and(inputA, inputB)
Arguments
inputA |
First input vector |
inputB |
Second input vector. Must be of the same dimension as inputA. |
Value
Returns the Fuzzy AND of two input values in a vector.
Examples
fuzzy_and(0, -1) # = -1
fuzzy_and(0, 1) # = 0
fuzzy_and(1, 2) # = 1
fuzzy_and(1, 1) # = 1
fuzzy_and(c(0.5, 0.75), c(1.5, 1)) # = c(0.5,0.75)
Fuzzy Norm
Description
Fuzzy Norm
Usage
fuzzy_norm(input)
Arguments
input |
The input (vector) data observation |
Value
Returns the Fuzzy norm results of input values
Examples
a = c(-1,-3,4,5)
fuzzy_norm(a) # = 13
Linear Algebra for Euclidean distance
Description
Linear Algebra for Euclidean distance
Usage
linalg_norm(inputA, inputB)
Arguments
inputA |
First input vector |
inputB |
Second input vector. Must be of the same dimension as inputA. |
Value
Returns the calculation results by squares of distances between two input values
Examples
a <- c(-3,-2,-1,3,3,2,3)
b <- c(-3,-2,-1,0,1,2,3)
linalg_norm(a,b) # = 3.605
Match function
Description
Match function
Usage
match_function(input, category_w)
Arguments
input |
The input (vector) data observation |
category_w |
The current category weight |
Value
Returns the vector of match Sj that will be used to check the vigilance parameter
Sample Data for simulate analysis data (Using IPM 2019)
Description
A real data of Human Development Index (Indeks Pembangunan Manusia) of West Java, Indonesia 2019
Usage
simulatedataIPM
Format
A tibble with 27 observation as region and 4 column as variables, which are:
- "AHH"
a value of Life expectancy (Angka Harapan Hidup)
- "HLS"
a value of Expected Years of Schooling (Harapan Lama Sekolah)
- "RLS"
a value of Mean Years of Schooling (Rata-rata Lama Sekolah)
- "Pengeluaran"
a value of Expenditure (Pengeluaran)
Source
Update weight
Description
Update weight
Usage
update_weight(input, category_w, beta)
Arguments
input |
The input (vector) data observation |
category_w |
The current category weight |
beta |
Learning rate in (0,1) |
Value
Returns the updated weight
Vigilance check
Description
Vigilance check
Usage
vigilance_check(input, category_w, rho)
Arguments
input |
The input (vector) data observation |
category_w |
The current category weight |
rho |
Vigilance parameter (0,1) |
Value
Returns Boolean value (True or False) as a result of checking the match Sj vector passed the vigilance parameter or not