calcSFCMBelongMatrix: Calculate the membership matrix (spatial version)

In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification

Calculate the membership matrix (spatial version)

Description

Calculate the membership matrix (spatial version) according to a set of centroids, the observed data, the fuzziness degree a neighbouring matrix and a spatial weighting term

Usage

calcSFCMBelongMatrix(centers, data, wdata, m, alpha, sigmas, wsigmas)

Arguments

centers	A matrix or a dataframe representing the centers of the clusters with p columns and k rows
data	A matrix representing the observed data with n rows and p columns
wdata	A matrix representing the lagged observed data with n rows and p columns
m	A float representing the fuzziness degree
alpha	A float representing the weight of the space in the analysis (0 is a typical fuzzy-c-mean algorithm, 1 is balanced between the two dimensions, 2 is twice the weight for space)
sigmas	A numeric vector for calculating the robust version of the FCM. Filled with ones if the classical version is required
wsigmas	Same as sigmas, but calculated on the spatially lagged dataset

Value

A n * k matrix representing the belonging probabilities of each observation to each cluster

geocmeans documentation built on Sept. 12, 2023, 9:06 a.m.