Data Driven Resource Discovery Using Self-Organising Maps-- An Introduction

摘要

The self-organising map (SOM) is an exploratory data mining technique that is both non-traditional and underutilised. Methodologies based on SOM tend to be data-driven and unsupervised, which makes them ideal to assist in the integrated analysis and interpretation of complex and disparate 'mineral exploration' data sets. While traditional statistical multivariate approaches have difficulty with relationships that are non-linear and data distributions that are not normal, SOM-based data mining procedures are useful in these circumstances. In a SOM analysis each sample is treated as a vector in a data space defined by the variables; and measures of vector similarity, such as the dot-product or Euclidean distance, are used to order or segment a data set into naturally occurring populations. These groupings are positioned as nodes, or groups of nodes, on a 2D rectilinear representation of the multi-dimensional 'data space', which is the 'self organised map'. While it is common not to include a sample's locational information in the actual SOM analysis, such information can be used to display the spatial location of samples coded by their SOM node or cluster.