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 (Springer, 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.
Keywords: Sistemas autoorganizativos; Procesado de imágenes; Análisis cluster; Minería de datos; Inteligencia artificial; Redes neuronales (Informática); Self-organization; Clustering; Text mining; Image segmentation