HClust

Hierarchical Clustering for Julia, similar to R's hclust()

Status

Please note this package has now been merged into Clustering.jl

This repository shows any additional work-in-progress. Clustering involves doing a lot of admin, and it is easy to make an error. I've tested the results for medium sized clusters (up to 250---5000) elements, for the following methods:

method	validated at matrix size	time	validated
:single	5000	1.3	OK
:complete	2500	4.5	OK
:average	2500	4.5	OK

Usage

d = rand(1000,1000)
d += d'  ## make sure distance matrix d is symmetric (this is optional)
h = hclust(d, :single)

hclust()

hclust(distance::Matrix, method::Symbol)

Performs hierarchical clustering for distance matrix d (which is forced to be symmetric), using one of three methods:

• :single: cluster distance is equal to the minimum distance between any of the members
• :average: cluster distance is equal to the mean distance between any of the cluster's members
• :complete: cluster distance is equal to the maximum distance between any of the members.

The output of hclust() is an object of type Hclust with the fields

• merge the clusters merged in order. Leafs are indicated by negative numbers
• height the distance at which the merges take place
• order a preferred grouping for drawing a dendogram. Not implemented, always [1:n].
• labels labels of the clusters. Not implemented, now always [1:n]
• method the name of the clustering method.

cutree()

cutree(cl:Hclust; h, k)

Cuts the cluster tree at height h or amounting to k clusters.

The output is a vector of indices. The nth element in this vector indicates the cluster that this data point belongs to.