On the use of Wasserstein metric in topological clustering of distributional data

• URL: http://arxiv.org/abs/2109.04301v1
• Date: Thu, 9 Sep 2021 14:27:15 GMT
• Title: On the use of Wasserstein metric in topological clustering of distributional data
• Authors: Gu\'ena\"el Cabanes, Youn\`es Bennani, Rosanna Verde and Antonio Irpino
• Abstract summary: This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space. Related to the kind of data, a suitable dissimilarity measure between distributions is introduced: the $L_2$ Wasserstein distance. Moreover, the number of clusters is not fixed in advance but it is automatically found according to a local data density estimation in the original space. Applications on synthetic and real data sets corroborate the proposed strategy.

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