Overview

The main entry point to all the algorithms is the cluster function in each of the clustering.core.qt, clustering.core.k-means and clustering.core.hierarchical namespaces. Generally all cluster variants require a distance function and a dataset (sequence/collection/…), where:

• the distance function should take two dataset items and perform some scalar measure of distance between the two points. Typical applications include euclidean distance, manhattan distance or pearson distance.
• the dataset should be a SEQable collection of data points that would be clustered.

The K-means and hierarchical clustering algorithms also require an averaging function that takes a number of dataset items and creates an “average” based on those items.

N-dimensional clustering

The following examples show distance and averaging functions on a 1-dimensional dataset comprising of dates. For most numeric datasets, any of the distance measures in clustering.distance would be sufficient; these implementations can handle arbitrarily-large n-dimensional datasets.

For non-numeric data, it would be necessary to either provide a mapper method to ‘convert’ non-numeric values into a meaningful numeric value (and then use the provided distance functions), or implement your own custom distance function.

Likewise, there exists a generic clustering.average namespace that operates on arbitrarily-large n-dimensional numeric datasets.