IEEE Transactions on Pattern Analysis and Machine Intelligence
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A Time Adaptive Self-Organizing Map for Segmenting Color Images into Exactly Two Regions
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps
Intelligent Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing trees and forests: a powerful tool in pattern clustering and recognition
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
TASOM: a new time adaptive self-organizing map
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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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.