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On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps
- Source :
- AI 2009: Advances in Artificial Intelligence ISBN: 9783642104381, Australasian Conference on Artificial Intelligence
- Publication Year :
- 2009
- Publisher :
- Springer Berlin Heidelberg, 2009.
-
Abstract
- 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.
- Subjects :
- Red–black tree
Fractal tree index
Tree rotation
K-ary tree
Binary tree
Theoretical computer science
Computer science
Optimal binary search tree
Interval tree
Cartesian tree
Search tree
Random binary tree
Treap
k-d tree
Tree traversal
Binary search tree
Ternary search tree
Binary expression tree
Algorithm
Self-balancing binary search tree
Order statistic tree
Subjects
Details
- ISBN :
- 978-3-642-10438-1
- ISBNs :
- 9783642104381
- Database :
- OpenAIRE
- Journal :
- AI 2009: Advances in Artificial Intelligence ISBN: 9783642104381, Australasian Conference on Artificial Intelligence
- Accession number :
- edsair.doi...........400431194a0e32bca6401c625ec9c0fb