HClust.jl

Hierarchical Clustering for Julia, similar to R's hclust()
Popularity
8 Stars
Updated Last
1 Year Ago
Started In
January 2014

HClust

Build Status

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.