in this project we are going to implement fuzzy c-means clustering algorithm in java. first we are going to give a brief look at the algorithm steps, then dive into details of methods we used.
for more details you can visit this link : https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html

1. initial membership
2. calculate centroides value
3. update membership values
4. check convergence

there is a loop berween step 2 til step 4, after specific number of iterations or satisfying error condition the algorithm will be stop.

the source code contains a class named "FuzzyClustering" that has some fields and methods, in the below we will give a brief information about them.
here is the fields of FuzzyClustering.java class

U matrix

- matrix of membership values with n * m dimensiones (n = dataset size and m = cluster number size)

iteration

- #iteration that algorithm perform calculation

fuzziness

- value of parameter M in c-mean formula

epsilon

- threshold of error between current membership values and prevoius step

next we will describe arguments and functionality of methods

createRandomData

- this function get dataset size, min and max range, number of clusters and generate random number with gaussian distribution

assignInitialMembership

- initialize first values for membership of data

calculateClusterCenters

- this function will calculate value of centroids

updateMembershipValues

- this function will update membership values depends on current centroids value

checkConvergence

- this function will calculate norm 2 of current U matrix and previous U matrix

after running algorithm two file will be generate, "data_set.csv" and "cluster_center.csv" that contains random data and calculated centroids