FKM.noise: Fuzzy k-means with noise cluster

Description

Performs the fuzzy k-means clustering algorithm with noise cluster.

FKM.noise (X, k, m, delta, RS, stand, startU, conv, maxit)

Matrix or data.frame

Number of clusters (default: 2)

Parameter of fuzziness (default: 2)

delta

Noise distance (default: average squared Euclidean distance between objects and prototypes from FKM using the same values of k and m)

Number of (random) starts (default: 1)

stand

Standardization: if stand=1, the clustering algorithm is run using standardized data (default: no standardization)

startU

Rational starting point for the membership degree matrix U (default: no rational start)

conv

Convergence criterion (default: 1e-9)

maxit

Maximum number of iterations (default: 1e+6)

Object of class fclust, which is a list with the following components:
UMembership degree matrix
HPrototype matrix
FArray containing the covariance matrices of all the clusters (NULL for FKM.noise)
clusMatrix containing the indices of the clusters where the objects are assigned (column 1) and the associated membership degrees (column 2)
medoidVector containing the indices of the medoid objects (NULL for FKM.noise)
valueVector containing the loss function values for the RS starts
cputVector containing the computational times (user times) for the RS starts
iterVector containing the numbers of iterations for the RS starts
kNumber of clusters
mParameter of fuzziness
entDegree of fuzzy entropy (NULL for FKM.noise)
vpVolume parameter (NULL for FKM.noise)
deltaNoise distance
standStandardization (Yes if stand=1, No if stand=0)
XcaData used in the clustering algorithm (standardized data if stand=1)
XRaw data
callMatched call

Dave' R.N., 1991. Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 657-664.

data(butterfly)
clust=FKM.noise(butterfly,RS=5,delta=3)

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