The pairing heap: a new form of self-adjusting heap
Algorithmica
A new point-location algorithm and its practical efficiency: comparison with existing algorithms
ACM Transactions on Graphics (TOG)
Hierarchical Growing Cell Structures: TreeGCS
IEEE Transactions on Knowledge and Data Engineering
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
The VLDB Journal — The International Journal on Very Large Data Bases
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Growing self-reconstruction maps
IEEE Transactions on Neural Networks
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This paper presents optimization techniques that substantially speed up the Growing Neural Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a subject of an intensive research interest in recent years as it is used in various practical applications. However, a poor time performance on large scale problems requiring neural networks with a high amount of nodes can be a limiting factor for further applications (e.g., cluster analysis, classification, 3-D reconstruction) or a wider usage. We propose two optimization techniques that are aimed exclusively on an efficient implementation of the GNG algorithm internal structure rather than on a modification of the original algorithm. The proposed optimizations preserve all properties of the GNG algorithm and enable to use it on large scale problems with reduced computational requirements in several orders of magnitude.