Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances

Abstract : The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This paper presents a self-organizing map for interval-valued data based on adaptive Mahalanobis distances in order to do clustering of interval data with topology preservation. Two methods based on the batch training algorithm for the self-organizing maps are proposed. The first method uses a common Mahalanobis distance for all clusters. In the second method, the algorithm starts with a common Mahalanobis distance per cluster and then switches to use a different distance per cluster. This process allows a more adapted clustering for the given data set. The performances of the proposed methods are compared and discussed using artificial and real interval data sets.
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Contributor : Alexandra Siebert <>
Submitted on : Tuesday, September 17, 2013 - 9:51:00 AM
Last modification on : Thursday, March 29, 2018 - 11:06:05 AM

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Chantal Hajjar, Hani Hamdan. Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Neural Networks, Elsevier, 2013, 46, pp.124-132. ⟨10.1016/j.neunet.2013.04.009⟩. ⟨hal-00862571⟩

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