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Journal articles
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.
https://hal-supelec.archives-ouvertes.fr/hal-00862571
Contributor : Alexandra Siebert Connect in order to contact the contributor
Submitted on : Tuesday, September 17, 2013 - 9:51:00 AM
Last modification on : Monday, December 14, 2020 - 12:38:06 PM
Contributor : Alexandra Siebert Connect in order to contact the contributor
Submitted on : Tuesday, September 17, 2013 - 9:51:00 AM
Last modification on : Monday, December 14, 2020 - 12:38:06 PM