Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
ACM Computing Surveys (CSUR)
Introduction to Algorithms
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)
The Visual Computer: International Journal of Computer Graphics
The growing neural gas and clustering of large amounts of data
Optical Memory and Neural Networks
Growing neural gas efficiently
Neurocomputing
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The Growing Neural Gas algorithm (GNG) is a well-known classification algorithm that is capable of capturing topological relationships that exist in the input data. Unfortunately, simple implementations of the GNG algorithm have time complexity O(n^2), where n is the number of nodes in the graph. This fact makes these implementations impractical for use in production environments where large data sets are used. This paper aims to propose an optimized implementation that breaks the O(n^2) barrier and that addresses data in high-dimensional spaces without changing the GNG semantics. The experimental results show speedups of over 50 times for graphs with 200,000 nodes.