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Title: Exploratory data analysis and foreground detection with the growing hierarchical neural forest
Authors: Palomo, Esteban J.
López-Rubio, Ezequiel
Ortega Zamorano, Francisco 
Benítez-Rochel, Rafaela
UNESCO Clasification: 1203 Ciencia de los ordenadores
120312 Bancos de datos
Keywords: Self-organization
Text mining
Image segmentation
Issue Date: 2020
Journal: Neural Processing Letters 
Abstract: In this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces three important limitations regarding the self-organizing map: fixed size, fixed topology, and lack of hierarchical representation for input data. Hence, the GHNF is especially amenable to datasets containing clusters where each cluster has a hierarchical structure since each tree of the GHNF forest can adapt to one of the clusters. Experimental results show the goodness of our proposal in terms of self-organization and clustering capabilities. In particular, it has been applied to text mining of tweets as a typical exploratory data analysis application, where a hierarchical representation of concepts present in tweets has been obtained. Moreover, it has been applied to foreground detection in video sequences, outperforming several methods specialized in foreground detection.
ISSN: 1370-4621
DOI: 10.1007/s11063-020-10360-2
Source: Neural Processing Letters [ISSN 1370-4621], n. 52, p. 2537-2563
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