Batch self-organizing maps based on city-block distances for interval variables
De Melo, Filipe M.; Bertrand, Patrice; De A. T. De Carvalho, Francisco (2012), Batch self-organizing maps based on city-block distances for interval variables. https://basepub.dauphine.fr/handle/123456789/9692
TypeDocument de travail / Working paper
External document linkhttp://hal.archives-ouvertes.fr/hal-00706519
MetadataShow full item record
Abstract (EN)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.
Subjects / KeywordsAdaptive distances; City-block distances; Interval-valued data; Self-organizing maps
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