Clustering with a Novel Non Euclidean Relative Distance

[Up] [Top]

Documentation for package ‘RelativeDistClust’ version 0.1.0

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