Introduction

API

The package som-learn follows the scikit-learn API using the base clusterer functionality. More specifically:

It implements a fit method to learn from data:

clusterer = object.fit(data)

it implements a fit_predict method to predict cluster labels:

cluster_labels = object.fit_predic(data)

SOM clusterer accepts the following inputs:

  • data: array-like (2-D list, pandas.Dataframe, numpy.array) or sparse matrices;

Self-Organizing Map

A Self-Organizing Map (SOM), also called Kohonen map, is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space, called a map. SOM can be used as a clustering algorithm as well as a dimensionality reduction method. The SOM class is a scikit-learn compatible wrapper class around somoclu’s implementation of SOM.