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.

本文提出了一种新的自组织人工神经网络，称为生长层次神经森林（GHNF）。GHNF是基于成长中的神经森林的层次模型，该模型是基于树的模型，它学习一组树（森林）而不是一般的图，从而使森林可以扩大大小。这样，GHNF在自组织映射方面面临三个重要限制：固定大小，固定拓扑和缺乏输入数据的层次表示。因此，GHNF特别适合包含群集的数据集，其中每个群集具有分层结构，因为GHNF森林的每个树都可以适应其中一个群集。实验结果显示了我们的建议在自组织和聚类能力方面的优势。尤其是，它已作为典型的探索性数据分析应用程序应用于推文的文本挖掘，其中已获得推文中概念的层次表示。此外，它已应用于视频序列中的前景检测，其性能优于几种专门用于前景检测的方法。