R: Clustering with a Novel Non Euclidean Relative Distance

# Clustering with a Novel Non Euclidean Relative Distance

## Documentation for package ‘RelativeDistClust’ version 0.1.0

- DESCRIPTION file

## Help Pages

add_unique_numbers | Add values to a vector if they are not already in it
add_unique_numbers2 | Add one value to a vector if it is not already there
AitchisonDistance | Aitchison distance
BrayCurtisDissimilarity | Bray-Curtis dissimilarity
centers_function_mean | Center of a cluster using the mean
centers_function_RelativeDistance | Center of a cluster when the Relative distance is used.
ClustPlot | Plotting the clustring results
DaviesBouldinIndex | Davies-Bouldin index
DistanceBetweenGroups | Distance between groups
DistanceSameGroup | Distance between points in the same group
Dist_IC1_IC2 | Finding IC1 and IC2 from a distance matrix
DosMinimos | Finding the two smallest values for each row of a matrix
DunnIndex | Dunn's index
d_i_other_group | Distance between a point and a group
ECDentroCluster | Sum of squared errors within the cluster
ECDentroCluster3 | Sum of errors within the cluster
encontrar_componente | Finding the component in the list that contains a value
Euclideandistance | Euclidean distance
Hartigan_and_Wong | Flexibilization of the Hartigan and Wong algorithm
Hartigan_and_Wong_total | Hartigan and Wong algorithm
init_centers_hw | Initializing the centers
init_centers_random | Initializing the centers
kmedois_distance | K-Medoids
ManhattanDistance | Manhattan distance
NEC | Non Euclidean Algorithm to Cluster
NEC_total | NEC algorithm
Number_of_failes | Comparison of groupings
RelativeDistance | Relative Distance
Silhouette | Silhouette
Step4 | Step 4 of the Hartigan and Wong algorithm
Step6 | Step 6 of the Hartigan and Wong algorithm
to_minimize | Sum of the distance between the points in a group and a given center.
vector_a_lista | Vector to list