Data Mining Through Self Organising Maps Applied on Select Exchange Rates
The self organising maps are gaining popularity as they help in organizing the haphazard data in topological maps. They conserve space in storing, help in pattern identification, matching, recognition, data mining etc. The Neural Networks designed by Hopfield is applied in this paper to organize the returns produced by seven exchange rates by the competitive Kohonen algorithm. Our analysis produces interesting self organizing maps for these currency returns. All exchange rate returns are nicely organized in a solid tight group and placed at the center of the boundary rectangle except for US dollar, European Euro and Korean Won. One weekly grouped return fall outside the boundary rectangle for these three exchange rates. These grouped returns are outliers which could have germinated by significant information or an economic event happened in these countries.
