Self- and Super-organizing Maps inR: ThekohonenPackage

Abstract

In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.

Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)

“…SOMs can be used to segment and visualize similarities between diverse data types in a multi-dimensional space. It can be seen as a spatially-constrained form of k-means clustering [46]. The data-driven algorithm belongs to the group of unsupervised artificial neural networks and can handle non-linear relationships and non-Gaussian data distributions [47].…”

“…Not only the vector, but also the neighborhood is shifted. During training, the spatial extent of the neighborhood shrinks, and only the winning units are adopted in the end [46]. A detailed description can be found in Kohonen [47] and Bedini [3].…”

Unsupervised model-based clustering for typological classification of Middle Bronze Age flanged axes

“…Practically, the codebook vectors are initiated at random. They are then progressively displaced by an iterative process, following an algorithm reasonably similar to the one applied for the widely used k-means clustering (more details about the procedure can be found in Wehrens and Buydens, 2007;Wehrens, 2011;Kung, 2014). It should be noticed however that different tunings (number of codebook vectors, type of topology used, etc.)…”

“…In practice, acquisition and statistical treatments were performed using the set of functions found in Claude (2008), together with the MASS (Venables and Ripley, 2002), mclust Raftery, 2002, 2007;Fraley et al, 2012), momocs (Bonhomme et al, 2014), kohonen (Wehrens and Buydens, 2007), and ks (Duong, 2007) packages, all written for the free R software (R Core Team, 2014). Multinomial scan statistics and geographical mapping were performed using SaTScan v9.3.1 (http://www.satscan.org/; Kulldorff et al, 1998) and Quantum GIS 2.6.1.…”

Tendances observées sur les régimes hydrologiques de l’Arc Alpin

“…Cette large diversité physiographique de bassins versants conduit à un large spectre de régimes hydrologiques. La classification des stations par type de régime est réalisée selon la répartition de leurs débits mensuels interannuels (Figure 2), en utilisant la méthode de classification de Kohonen [Wehrens et Buydens, 2007]. Neuf types de régime sont présents dans notre jeu de données, les régimes dits purs : glaciaire, nivo-glaciaire, et nival, puis les régimes appelés nival de transition (subdivisé en deux sous catégories), les régimes « plats » d'influences hydrologiques multiples appelés régimes complexes (subdivisés en deux sous catégories), et enfin les régimes dits mixtes soumis aux influences conjuguées (en proportions variables) de précipitations liquides et solides : nivo-pluvial et pluvio-nival [Pardé, 1955].…”