Self-adjusting binary search trees
Journal of the ACM (JACM)
Efficient maintenance of binary search trees
Efficient maintenance of binary search trees
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Self-Organizing Binary Search Trees
Journal of the ACM (JACM)
Self-Organizing Maps
Introduction to Algorithms
Adaptive Structuring of Binary Search Trees Using Conditional Rotations
IEEE Transactions on Knowledge and Data Engineering
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
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
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
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We present a strategy by which a Self-Organizing Map (SOM) with an underlying Binary Search Tree (BST) structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time , guaranteeing a decrease in the Weighted Path Length (WPL) of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution, and additionally, the neighborhood properties of the neurons suit the best BST that represents the data.