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oai:riuma.uma.es:10630/394162026-02-03T11:25:26Zcom_10630_2254col_10630_37953

Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest. Palomo-Ferrer, Esteban José López-Rubio, Ezequiel Ortega-Zamorano, Francisco Benítez-Rochel, Rafaela Sistemas autoorganizativos Procesado de imágenes Análisis cluster Minería de datos Inteligencia artificial Redes neuronales (Informática) 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. 2025-07-18T10:25:42Z 2025-07-18T10:25:42Z 2020 journal article Palomo, E.J., López-Rubio, E., Ortega-Zamorano, F. et al. Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest. Neural Process Lett 52, 2537–2563 (2020). https://doi.org/10.1007/s11063-020-10360-2 https://hdl.handle.net/10630/39416 10.1007/s11063-020-10360-2 eng open access Springer