Data Mining Using Self-Organizing Kohonen Maps: A Technique for Effective Data Clustering & Visualization

Exploratory data mining using artificial neural networks offers an alternative dimension to data mining, in particular techniques geared towards data clustering and classification. In this paper, we argue the case for using neural networks as a viable data mining tool that can provide statistical insights and models from large data-sets. We demonstrate how Self-Organizing Kohonen Maps, an unsupervised learning neural network paradigm, can be efficaciously used for data mining purposes, in particular for data clustering applications. We show that high-dimensional data can be projected to a lower dimension and that data can be clustered together whilst preserving essential information. The Kohonen Map based data-clustering technique is applied to the 1991 World Bank social economics indicators, to show how multi-dimensional data sets can be reduced to two-dimensional (feature) maps, manifesting clusters of similar data items.