Batch self-organizing maps based on city-block distances for interval variables

The Kohonen Self Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Batch SOM algorithms based on adaptive and non-adaptive city-block distances, suitable for objects described by interval-valued variables, that, for a fixed epoch, optimizes a cost function, are presented. The performance, robustness and usefulness of these SOM algorithms are illustrated with real interval-valued data sets.