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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-00706519
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Submitted on : Monday, June 11, 2012 - 12:49:18 AM
Last modification on : Tuesday, July 31, 2018 - 3:04:02 PM
Long-term archiving on: Wednesday, September 12, 2012 - 2:21:50 AM

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Francisco de A. T. de Carvalho, Patrice Bertrand, Filipe de Melo. Batch self-organizing maps based on city-block distances for interval variables. 2012. ⟨hal-00706519⟩

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