Clustering of Interval Data Using Self-Organizing Maps – Application to Meteorological Data
Chapter
Abstract
The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This chapter presents a self-organizing map to do unsupervised clustering for interval data. This map uses an extension of the Euclidian distance to compute the proximity between two vectors of intervals where each neuron represents a cluster. The performance of this approach is then illustrated and discussed while applied to temperature interval data coming from Chinese meteorological stations. The bounds of each interval are the measured minimal and maximal values of the temperature. In the presented experiments, stations of similar climate regions are assigned to the same neuron or to a neighbor neuron on the map.
Keywords
Input Vector Hausdorff Distance Interval Data Neighborhood Function Neighborhood Radius
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