module HClust

Overview

The HClust module provides methods for fast hierarchical agglomerative clustering featuring efficient linkage algorithms.

Cluster analysis or clustering arrange a set of objects into distinct groups or clusters such that the elements within a cluster are more similar to each other than those in other clusters based on a given criterion. The latter is defined as a measure of dissimilarity between the objects, and it's usually encoded in a DistanceMatrix. Hierarchical agglomerative clustering builds a hierarchy of clusters, where each object or element starts in its own cluster, and then they are progressively merged forming a hierarchy among them. The dissimilarities bewteen a newly-created cluster and all other clusters are updated after each merging step. The dissimilarity between two clusters is dictated by the chosen linkage criterion or rule. The obtained hierarchy is often encoded as a Dendrogram, which can be cut at a given dissimilarity value to obtain flat clusters such that the elements of a flat cluster have a cophenetic distance no greater than the given value (see Dendrogram#flatten).

The standard algorithm has a time complexity of Θ(N³), which is implemented by the .primitive method. However, optimal algorithms are provided based on the work of Daniel Müllner [1], which describes several optimizations over the standard algorithm (implemented by the .generic method), and fast methods for special cases such as the minimum spanning tree (MST) method (.mst) for single linkage and nearest-neighbor-chain (NN-chain) method (.nn_chain) for average, complete, Ward's, and weighted linkage. Thus, the best-case complexity can be reduced to Θ(N²), where, in practice, the runtime of the general case is close to this.

The current implementation is heavily based on Müllner's own implementation found in the fastcluster C++ library, and parts of the implementation were also inspired by the kodama Rust crate (code for updating distances) and SciPy Python library (code for generating flat clusters).

The most relevant types and methods for practical usage are the following:

• .cluster performs hierarchical clustering of a set of elements using the pairwise dissimilarities returned by the given block.
• .linkage performs hierarchical clustering based on a pairwise DistanceMatrix returning a Dendrogram as output.
• Rule is an enum that determines the linkage criterion or rule.
• Dendrogram represents a step-wise dendrogram that encodes the hierarchy of clusters obtained from hierarchical clustering.
• Dendrogram#flatten returns a list of flat clusters.

The available linkage rules are:

• Rule::Average or UPGMA method.
• Rule::Centroid or UPGMC method.
• Rule::Complete or farthest neighbor method.
• Rule::Median or WPGMC method.
• Rule::Single or nearest neighbor method.
• Rule::Ward's minimum variance method
• Rule::Weighted or WPGMA method.

See Rule documentation for details.

Clustering example

Let's first define a list of random coordinates in 3D space as an example case:

coords = [
  [-0.30818828, 2.70462841, 1.84344886],
  [2.9666203, -1.39874721, 4.76223947],
  [3.21737027, 4.09489028, -4.60403434],
  [-3.51140292, -0.83953645, 2.31887739],
  [2.08457843, 4.24960773, -3.91378835],
  [2.88992367, -0.97659082, 0.75464131],
  [0.43808545, 3.70042294, 4.99126146],
  [-1.71676206, 4.93399583, 0.27392482],
  [1.12130963, -1.09646418, 1.45833231],
  [-3.45524705, 0.92812111, 0.15155981],
]
labels = (0...coords.size).to_a # for demonstration purposes

We'd like to group them by the proximity to each other defined by Euclidean distance:

def euclidean(u, v)
  Math.sqrt (0...u.size).sum { |i| (u[i] - v[i])**2 }
end

Let's say we want to split the coordinates into groups with a distance no more than 4. The easiest way is to use the .cluster convenience method:

clusters = HClust.cluster(labels, 4) { |i, j| euclidean(coords[u], coords[v]) }
clusters.size # => 3
clusters      # => [[0, 3, 6, 7, 9], [1, 5, 8], [2, 4]]

The method receives the elements to be clustered, a distance cutoff, and a block that receives two of the elements and must return the dissimilarity between them. We observe that the coordinates can be grouped into 3 distinct clusters containing 5, 3, and 2 elements, respectively. The order of the clusters is arbitrary and depends on the order of the elements.

Alternatively, one can set the maximum number of clusters to be generated instead of a distance cutoff using the named argument into::

clusters = HClust.cluster(labels, into: 2) { |i, j| euclidean(coords[i], coords[j]) }
clusters.size # => 2
clusters      # => [[0, 1, 3, 5, 6, 7, 8, 9], [2, 4]]

As stated above, the linkage rule dictates how the dissimilarities are updated upon merging two clusters during the clustering procedure. Therefore, the clustering output will depend on the selected rule. In turn, the choice of the linkage rule depends both on the problem domain and performance requirements. By default, the single linkage is used throughout the code but it can be given as the third (optional) argument to the cluster methods if required.

clusters = HClust.cluster(labels, 2, :centroid) { |i, j|
  euclidean(coords[i], coords[j])
}
clusters.size # => 5
clusters      # => [[0, 7], [1, 5, 8], [2, 4], [3, 9], [6]]

Note the different number of clusters and composition obtained with the centroid linkage comparted to the previous result using the single linkage.

Advanced usage

Using the .cluster methods is enough in most cases, albeit obtaining the dendrogram can be useful for visualization purposes or testing different clustering arguments without recomputing the dissimilarities.

Under the hood, the .cluster methods construct a DistanceMatrix with the given block, invoke the .linkage method, and then call Dendrogram#flatten on the obtained dendrogram with the given argument. The latter returns an array of clusters, each containing a list of indexes that can be used to fetch the original elements. The equivalent code to the above example would be:

dism = DistanceMatrix.new(coords.size) { |i, j| euclidean(coords[i], coords[j]) }
dendrogram = linkage(dism)
clusters = dendrogram.flatten(4)

The dendrogram represents the hierarchy as a series of merge steps, which contain the merged clusters and computed dissimilarity. For instance, let's see what the dendrogram looks like:

pp dendrogram.steps.map { |s| [s.clusters[0], s.clusters[1], s.distance, 0.0] }

will print:

# cluster1, cluster2, dissimilarity, size (unused)
[[2, 4, 1.3355127737375514, 0.0],
 [5, 8, 1.9072352420201895, 0.0],
 [3, 9, 2.7973259058854163, 0.0],
 [0, 7, 3.068805125647255, 0.0],
 [6, 13, 3.384856775520168, 0.0],
 [1, 11, 3.796359969884454, 0.0],
 [12, 14, 3.9902939298123274, 0.0],
 [15, 16, 4.079225895869359, 0.0],
 [10, 17, 5.696974476555648, 0.0]]

The output can be copied into a Python terminal and visualized using the dendrogram function in SciPy or similar software. It would look something like:

  |       ________________
5 |      |                |
  |      |                |
4 |      |         _______|_______
  |      |      __|__         ____|___
3 |      |     |     |       |      __|__
  |      |     |     |      _|_    |    _|_
2 |      |     |    _|_    |   |   |   |   |
  |      |     |   |   |   |   |   |   |   |
1 |     _|_    |   |   |   |   |   |   |   |
  |    |   |   |   |   |   |   |   |   |   |
0 |    2   4   1   5   8   3   9   6   0   7

Using this graph, one can deduce the optimal cutoff for generating flat clusters, where a cutoff = 4 would produce indeed two clusters as shown above.

Defined in:

hclust.cr
hclust/dendrogram.cr
hclust/version.cr

Constant Summary

VERSION = {{ (`shards version \"/srv/crystaldoc.info/github-franciscoadasme-hclust-v1.0.0/src/hclust\"`).chomp.stringify }}

Class Method Summary

• .cluster(elements : Indexable(T), cutoff : Number, rule : Rule = :single, & : T, T -> Float64) : Array(Array(T)) forall T

  Clusters elements using the linkage rule rule based on the distances computed by the given block.

• .cluster(elements : Indexable(T), *, into count : Int, rule : Rule = :single, & : T, T -> Float64) : Array(Array(T)) forall T

  Clusters elements into count clusters or fewer using the linkage rule rule based on the distances computed by the given block.

• .generic(dism : DistanceMatrix, rule : Rule) : Dendrogram

  Perform hierarchical clustering based on the distances stored in dism using a fast generic linkage algorithm with the given linkage rule.

• .linkage(dism : DistanceMatrix, rule : Rule, reuse : Bool = false) : Dendrogram

  Returns the hierarchical clustering based on the pairwise distances dism using the linkage rule rule.

• .mst(dism : DistanceMatrix) : Dendrogram

  Perform hierarchical clustering based on the distances stored in dism using the minimum spanning tree (MST) algorithm.

• .nn_chain(dism : DistanceMatrix, rule : ChainRule) : Dendrogram

  Perform hierarchical clustering based on the distances stored in dism using the nearest-neighbor-chain (NN-chain) algorithm with the given linkage rule.

• .primitive(dism : DistanceMatrix, rule : Rule) : Dendrogram

  Perform hierarchical clustering based on the distances stored in dism using the classic algorithm with the given linkage rule.

Class Method Detail