Learning Hierarchical Clustering Algorithms
To explain agglomerative hierarchical clusterization algorithm by dist...
To execute agglomerative hierarchical clusterization algorithm by dist...
To show the formula and to return the Canberra distance.
To calculate the Canberra distance.
To calculate the Canberra distance applying weights .
To calculate the Canberra distance applying weights.
To show the formula of the Chebyshev distance.
To calculate the Chebyshev distance.
To calculate the Chebyshev distance applying weights.
To calculate the Chebyshev distance applying weights.
To explain how to calculate the distance between clusters.
To calculate the distance between clusters.
To explain how to calculate the distance by approach option.
To calculate the distance by approach option.
To explain how and why two clusters are complementary.
To check if two clusters are complementary
To explain how hierarchical correlation algorithm works.
To execute hierarchical correlation algorithm.
To calculate distances applying weights.
To calculate distances applying weights.
To explain the divisive hierarchical clusterization algorithm by dista...
To execute divisive hierarchical clusterization algorithm by distance ...
To show the Euclidean distance formula.
To calculate the Euclidean distance.
To calculate the Euclidean distance applying weights.
To calculate the Euclidean distance applying weights.
To explain how to get the clusters with minimal distance.
To get the clusters with minimal distance.
To explain how to get the clusters with maximal distance.
To get the clusters with maximal distance.
To explain how to initialize clusters for the divisive algorithm.
To initialize clusters for the divisive algorithm.
To initialize data, hierarchical correlation algorithm.
To initialize data, hierarchical correlation algorithm.
To display an image.
To initialize target, hierarchical correlation algorithm.
To initialize target, hierarchical correlation algorithm.
Matrix distance by distance type
Maximal distance
Maximal distance
Matrix distance by distance and approach type.
Matrix distance by distance and approach type.
Matrix distance by distance and approach type.
Matrix distance by distance and approach type.
To explain how to calculate the Manhattan distance.
To calculate the Manhattan distance.
To calculate the Manhattan distance applying weights.
To calculate the Manhattan distance applying weights.
Minimal distance
Minimal distance
To explain how to create a new cluster.
To create a new cluster.
To normalize weight values.
To normalize weight values.
To explain how to calculate the Octile distance.
To calculate the Octile distance.
To calculate the Octile distance applying weights.
To calculate the Octile distance applying weights.
To explain how to transform data into list
To transform data into list
To explain how to transform data into list
To transform data into list
To delete clusters grouped.
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step. It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. The package explains non expert users how hierarchical clustering algorithms work.