# calcNegentropyI: Negentropy Increment index In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification

 calcNegentropyI R Documentation

## Negentropy Increment index

### Description

Calculate the Negentropy Increment index of clustering quality.

### Usage

calcNegentropyI(data, belongmatrix, centers)


### Arguments

 data The original dataframe used for the clustering (n*p) belongmatrix A membership matrix (n*k) centers The centres of the clusters

### Details

The Negentropy Increment index \insertCiteda2020incrementalgeocmeans is based on the assumption that a normally shaped cluster is more desirable. It uses the difference between the average negentropy of all the clusters in the partition, and that of the whole partition. A smaller value indicates a better partition. The formula is:

NI=\frac{1}{2} \sum_{j=1}^{k} p_{i} \ln \left|{\boldsymbol{\Sigma}}_{j}\right|-\frac{1}{2} \ln \left|\boldsymbol{\Sigma}_{d a t a}\right|-\sum_{j=1}^{k} p_{j} \ln p_{j}

with a cluster, |.| the determinant of a matrix,

• j a cluster

• |.| the determinant of a matrix

• \left|{\boldsymbol{\Sigma}}_{j}\right| the covariance matrix of the dataset weighted by the membership values to cluster j

• \left|\boldsymbol{\Sigma}_{d a t a}\right| the covariance matrix of the dataset

• p_{j} the sum of the membership values to cluster j divided by the number of observations.

### Value

A float: the Negentropy Increment index

\insertAllCited

### Examples

data(LyonIris)
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img",
"TxChom1564","Pct_brevet","NivVieMed")
dataset <- sf::st_drop_geometry(LyonIris[AnalysisFields])
queen <- spdep::poly2nb(LyonIris,queen=TRUE)
Wqueen <- spdep::nb2listw(queen,style="W")
result <- SFCMeans(dataset, Wqueen,k = 5, m = 1.5, alpha = 1.5, standardize = TRUE)
calcNegentropyI(result$Data, result$Belongings, result\$Centers)


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