2026-05-28T22:17:08Zhttps://riuma.uma.es/rest/oai/request

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

00925njm 22002777a 4500 dc Palomo-Ferrer, Esteban José author López-Rubio, Ezequiel author Ortega-Zamorano, Francisco author Benítez-Rochel, Rafaela author 2020 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. 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 Sistemas autoorganizativos Procesado de imágenes Análisis cluster Minería de datos Inteligencia artificial Redes neuronales (Informática) Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest.