# correlationHC.details: To explain how hierarchical correlation algorithm works. In LearnClust: Learning Hierarchical Clustering Algorithms

## Description

To explain how the hierarchical correlation algorithm works.

## Usage

 ```1 2 3 4 5 6 7 8``` ```correlationHC.details( data, target = NULL, weight = c(), distance = "EUC", normalize = TRUE, labels = NULL ) ```

## Arguments

 `data` is a data frame with the main data. `target` is a data frame , a numeric vector or a matrix. Default value = NULL. `weight` is a numeric vector. Default value = empty vector. `distance` is a string. The distance type. Default value = Euclidean distance. `normalize` is a boolean parameter. If the user wants to normalize weights. Default value = TRUE. `labels` is a string vector. For the graphical solution. Default value = NULL.

## Details

This function explains the complete hierarchical correlation method. It explains the theoretical algorithm step by step.

1 - The function transforms data in useful object to be used.

2 - It creates the clusters.

3 - It calculates the distance from the target to every cluster applying the distance type given.

4 - It orders the distance in an increasing way.

5 - It orders the clusters according to their distance from the previous step

6 - It shows the clusters sorted and the distance used.

## Value

R object with a dendrogram, the sorted distances and the list with every cluster. Explanation.

## Author(s)

Roberto Alcántara roberto.alcantara@edu.uah.es

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```data <- matrix(c(1,2,1,4,5,1,8,2,9,6,3,5,8,5,4),ncol= 3) dataFrame <- data.frame(data) target1 <- c(1,2,3) target2 <- dataFrame[1,] weight1 <- c(1,6,3) weight2 <- c(0.1,0.6,0.3) correlationHC.details(dataFrame, target1) correlationHC.details(dataFrame, target1, weight1) correlationHC.details(dataFrame, target1, weight1, normalize = FALSE) correlationHC.details(dataFrame, target1, weight2, 'CAN', FALSE) ```