binClst-class: Binary Clustering Class

In EMbC: Expectation-Maximization Binary Clustering

Binary Clustering Class

Description

binClst is a generic multivariate binary clustering object.

Slots

X

The input data set. A multivariate matrix where each row is a data point and each column is an input feature (a variable).

U

A multivariate matrix with same dimension as X with the values of certainty associated to each corresponding value in X. Ceartainties assign reliability to the data points so that the less reliable is a data point the less its leverage in the clustering. By default certainties are set to one for all variables of all data points.

stdv

A numeric vector with variable specific values for minimum standard deviation.

m

The number of input features.

k

The number of clusters.

n

The number of observations (data points).

R

A matrix with the values delimiting each binary region (the Reference values).

P

A list with the GMM (Gaussian Mixture Model) parameters. Each element of the list corresponds to a component of the GMM and it is a named-sublist itself, with elements '$M' (the component's mean) and '$S' (the component's covariance matrix).

W

A n*k matrix with the likelihood weights.

A

A numeric vector with the clustering labels (annotations) for each data-point (the basic output data). Labels are assigned based on the likelihood weights. Only in case of equal likelihoods the delimiters are used as a further criterion to assign labels.

L

The values of likelihood at each step of the optimization process.

C

Default color palette used for the plots. Can be changed by means of the setc() function.

EMbC documentation built on Oct. 3, 2023, 5:07 p.m.