Binary tree time adaptive self-organizing map

• Published in: Neurocomputing (Amsterdam) Vol. 74; no. 11; pp. 1823 - 1839
• Main Author: Shah-Hosseini, Hamed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2011
• Subjects: Neural networks; Nonstationary; Self-organizing map; Time adaptive; Nonstationary; Time adaptive; Neural networks; Self-organizing map
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2010.07.037

An adaptive hierarchical structure called “Binary Tree TASOM” (BTASOM) is proposed, which resembles a binary natural tree having nodes composed of Time Adaptive Self-Organizing Map (TASOM) networks. The standard TASOM is almost as slow as the standard SOM and has a fixed number of neurons. The BTASOM is proposed to make the TASOM fast and adaptive in the number of its neurons. The BTASOM is the first proposed hierarchical structure that uses a binary tree topology with TASOM networks. The number of levels of the BTASOM and the number of its nodes are adaptive to the accuracy demanded by the application through user-defined parameters. Two versions of the BTASOM are used here: the first version in which every node has only one neuron, and the second version in which every node has exactly two neurons. Both versions are tested with different distributions, stationary and nonstationary, for data representation. The experiments show that the BTASOM can work with both stationary and nonstationary environments while increasing the adaptability and speed of the standard TASOM. Several performance measures demonstrate the superiority of the proposed BTASOM in comparison with some other hierarchical SOM-based networks for clustering and input space approximation.