Self-adjusting binary search trees
Journal of the ACM (JACM)
Efficient maintenance of binary search trees
Efficient maintenance of binary search trees
On the ordering conditions for self-organizing maps
Neural Computation
Neural networks: a systematic introduction
Neural networks: a systematic introduction
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Ordering of self-organizing maps in multidimensional cases
Neural Computation
Self-Organizing Binary Search Trees
Journal of the ACM (JACM)
Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Adaptive Structuring of Binary Search Trees Using Conditional Rotations
IEEE Transactions on Knowledge and Data Engineering
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Skeletal Shape Extraction from Dot Patterns by Self-Organization
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
TreeSOM: cluster analysis in the self-organizing map
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Real-time multiple people tracking using competitive condensation
Pattern Recognition
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
Self-organizing maps for the skeletonization of sparse shapes
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
A synthesised word approach to word retrieval in handwritten documents
Pattern Recognition
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
Self organizing maps constrained by data structures
Self organizing maps constrained by data structures
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Numerous variants of Self-Organizing Maps (SOMs) have been proposed in the literature, including those which also possess an underlying structure, and in some cases, this structure itself can be defined by the user. Although the concepts of growing the SOM and updating it have been studied, the whole issue of using a self-organizing Adaptive Data Structure (ADS) to further enhance the properties of the underlying SOM, has been unexplored. In an earlier work, we impose an arbitrary, user-defined, tree-like topology onto the codebooks, which consequently enforced a neighborhood phenomenon and the so-called tree-based Bubble of Activity (BoA). In this paper, we consider how the underlying tree itself can be rendered dynamic and adaptively transformed. To do this, we present methods by which a SOM with an underlying Binary Search Tree (BST) structure can be adaptively re-structured using Conditional Rotations (CONROT). These rotations on the nodes of the tree are local, can be done in constant time, and performed so as to decrease the Weighted Path Length (WPL) of the entire tree. In doing this, we introduce the pioneering concept referred to as Neural Promotion, where neurons gain prominence in the Neural Network (NN) as their significance increases. We are not aware of any research which deals with the issue of Neural Promotion. The advantage of such a scheme is that the user need not be aware of any of the topological peculiarities of the stochastic data distribution. Rather, the algorithm, referred to as the TTOSOM with Conditional Rotations (TTOCONROT), 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. These properties have been confirmed by our experimental results on a variety of data sets. We submit that all these concepts are both novel and of a pioneering sort.