# The self-organizing map

@article{Kohonen1998TheSM, title={The self-organizing map}, author={Teuvo Kohonen}, journal={Neurocomputing}, year={1998}, volume={21}, pages={1-6} }

Abstract An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

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#### 3,298 Citations

Robust Self-organizing Maps

- Computer Science
- CIARP
- 2004

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology… Expand

Yet another algorithm which can generate topography map

- Mathematics, Computer Science
- IEEE Trans. Neural Networks
- 1997

An algorithm to form a topographic map resembling to the self-organizing map is presented, based on defining an energy function which reveals the local correlation between neighboring neurons. Expand

Visualized Analysis of Multivariate Mixed-Type Data via an Extended Self-Organizing Map

SOM so that the model can directly process categorical values. Experimental results indicate that the extended model significantly facilitate analysis of mixed-type data.

EM algorithms for self-organizing maps

- Mathematics, Computer Science
- Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
- 2000

This work derives EM algorithms for self-organizing maps with and without missing values from the link between vector quantization and mixture modeling and explains why the former is better suited for the visualization of high-dimensional data. Expand

Codebook Clustering by Self-Organizing Maps for Fractal Image Compression

- Mathematics
- 1997

A fast encoding scheme for fractal image compression is presented. The method uses a clustering algorithm based on Kohonen's self-organizing maps. Domain blocks are clustered, yielding a… Expand

Grid topologies for the self-organizing map

- Mathematics, Computer Science
- Neural Networks
- 2014

Experimental results are presented for unsupervised clustering, color image segmentation and classification tasks, which show that the differences among the topologies are statistically significant in most cases, and that the optimal topology depends on the problem at hand. Expand

Convergence Rate in Intelligent Self-organizing Feature Map Using Dynamic Gaussian Function

- Computer Science
- KES
- 2006

This paper proposes a method improving the convergence speed and the convergence rate of the intelligent self-organizing feature map by adapting Dynamic Gaussian Function instead of using a Neighbor Interaction Set whose learning rate is steady during the training of the self- organising feature map. Expand

On Modified Self Organizing Feature Maps

- Computer Science
- 2018

This paper presents optimization of self-organizing feature maps by adjusting tunable parameters and in the iterative process by utilizing linear algebra concepts. A gradient rule is applied on the… Expand

Empirical Comparison between Two Computational Strategies for Topological Self-Organization

- Computer Science
- IDEAL
- 2003

This paper empirically compares the two strategies for topological self-organization and shows the new strategy to be shown to speed up theSelf-Organizing Map significantly. Expand

Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines

- Computer Science
- ArXiv
- 2015

This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which automates the very labor-intensive and therefore time-heavy and expensive and expensive process of computer programming called “smithing”. Expand

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A new computational algorithm, the probing algorithm, is introduced for the subproblem of finding the best matching unit in Kohonen's self-organization procedure and is compared to exhaustive search and to four other algorithms and shown to be roughly six to 10 times faster for the case of high-dimensional vectors. Expand

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A novel derivation of T. Kohonen's topographic mapping learning algorithm is presented, which provides a simple interpretation of the role of the neighborhood update scheme which is used and prescribes a vector quantizer by minimizing an L/sub 2/ reconstruction distortion measure. Expand

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The convergence and ordering of Kohonen's batch-mode self-organizing map with Heskes and Kappen's (1993) winner selection are proved and it is shown that when the neighborhood relation is doubly decreasing, order in the map is preserved. Expand

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Two innovations are discussed: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimum spanning tree, which provides a far better and faster approximation of prominently structured density functions. Expand

Enhancing supervised learning algorithms via self-organization

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The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than do conventional feedforward classifier networks when the decision regions are disjoint. Expand

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The author proposed a learning rule for a single-layer network of modules representing adaptive tables of the type formed by T. Kohonen's vector quantization algorithm (Rep. TKK-F-A601, Helsinki… Expand

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A neural-network clustering algorithm proposed by T. Kohonen (1986, 88) is used to design a codebook for the vector quantization of images and the results are compared with coded images when the cookbook is designed by the Linde-Buzo-Gray algorithm. Expand