fuzzy.CM {advclust}R Documentation

Fuzzy C-Means

Description

Fuzzy C-Means clustering Algorithm (Bezdek, 1984)

Usage

fuzzy.CM(X, K, m, max.iteration, threshold, member.init, RandomNumber = 0,
  print.result = 0)

Arguments

X

dataset (matrix/data frame)

K

number of cluster

m

fuzzyfier

max.iteration

maximum iteration for convergence

threshold

convergence criteria

member.init

membership object or matrix that will be used for initialized

RandomNumber

random number for start initializing

print.result

print result (9/1)

Details

This function perform Fuzzy C-Means algorithm by Bezdek (1984). Fuzzy C-Means is one of fuzzy clustering methods to clustering dataset become K cluster. Number of cluster (K) must be greater than 1. To control the overlaping or fuzziness of clustering, parameter m must be specified. Maximum iteration and threshold is specific number for convergencing the cluster. Random Number is number that will be used for seeding to firstly generate fuzzy membership matrix.

Clustering will produce fuzzy membership matrix (U) and fuzzy cluster centroid (V). The greatest value of membership on data point will determine cluster label. Centroid or cluster center can be use to interpret the cluster. Both membership and centroid produced by calculating mathematical distance. Fuzzy C-Means calculate distance with Euclideans norm.

Value

Fuzzy Clustering object

Slots

centroid

centroid matrix

distance

distance matrix

func.obj

function objective

call.func

called function

fuzzyfier

fuzzyness parameter

method.fuzzy

method of fuzzy clustering used

member

membership matrix

hard.label

hard.label

References

Balasko, B., Abonyi, J., & Feil, B. (2002). Fuzzy Clustering and Data Analysis Toolbox: For Use with Matlab. Veszprem, Hungary.

Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The Fuzzy C-Means Clustering Algorithm. Computers and Geosciences Vol 10, 191-203

Examples

fuzzy.CM(iris[,1:4],K=3,m=2,max.iteration=100,threshold=1e-5,RandomNumber=1234)

[Package advclust version 0.4 Index]