Exploring power transformer database using Self-Organising Maps (SOM) and Minimal Spanning Tree (MST)

Abstract

Data mining or exploration is part of a larger area of recent research in Artificial Intelligence and Information Processing and Management otherwise known as Knowledge Discovery in Database (KDD). The main aim here is to identify new information or knowledge from database in which the dimensionality or amount of data is so large that it is beyond human comprehension. Self-Organising Map and Minimal Spanning Tree are used to analyse power transformer database from one of the electric energy providers in Japan. Evaluation of the clusters generated by SOM is usually done by human eye. Due to its qualitative nature, the evaluator may either overestimate or underestimate the number of clusters formed on the map. With this approach, the exact number of clusters generated by the map cannot be confirmed because of the misinterpretation of the grey level expression. This paper looks at clustering with Minimal Spanning Tree (MST).