Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

SOM's mathematics

International audienceSince the discovery of the SOM's by T. Kohonen, many results that provide a better description of their behaviour have been found. Most of them are very convincing, but from a mathematical point of view, only a few are actually proved. In this paper, we make a review of some results that are still to be proved and give some framework to formulate various questions

Neural Networks 2006 Special Issue "Advances in Self-Organizing Maps-WSOM 05"

Special issue of Neural Networks Journal after the WSOM 05 ConferenceSpecial issue of Neural Networks Journal after the WSOM 05 ConferenceNeural Networks Volume 19, Issues 6-7, Pages 721-976 (July-August 2006) Advances in Self Organising Maps - WSOM'05 Edited by Marie Cottrell and Michel Verleysen 1. Advances in Self-Organizing Maps Pages 721-722 Marie Cottrell and Michel Verleysen 2. Self-organizing neural projections Pages 723-733 Teuvo Kohonen 3. Homeostatic synaptic scaling in self-organizing maps Pages 734-743 Thomas J. Sullivan and Virginia R. de Sa 4. Topographic map formation of factorized Edgeworth-expanded kernels Pages 744-750 Marc M. Van Hulle 5. Large-scale data exploration with the hierarchically growing hyperbolic SOM Pages 751-761 Jörg Ontrup and Helge Ritter 6. Batch and median neural gas Pages 762-771 Marie Cottrell, Barbara Hammer, Alexander Hasenfuß and Thomas Villmann 7. Fuzzy classification by fuzzy labeled neural gas Pages 772-779 Th. Villmann, B. Hammer, F. Schleif, T. Geweniger and W. Herrmann 8. On the equivalence between kernel self-organising maps and self-organising mixture density networks Pages 780-784 Hujun Yin 9. Adaptive filtering with the self-organizing map: A performance comparison Pages 785-798 Guilherme A. Barreto and Luís Gustavo M. Souza 10. The Self-Organizing Relationship (SOR) network employing fuzzy inference based heuristic evaluation Pages 799-811 Takanori Koga, Keiichi Horio and Takeshi Yamakawa 11. SOM's mathematics Pages 812-816 J.C. Fort 12. Performance analysis of LVQ algorithms: A statistical physics approach Pages 817-829 Anarta Ghosh, Michael Biehl and Barbara Hammer 13. Self-organizing map algorithm and distortion measure Pages 830-837 Joseph Rynkiewicz 14. Understanding and reducing variability of SOM neighbourhood structure Pages 838-846 Patrick Rousset, Christiane Guinot and Bertrand Maillet 15. Assessing self organizing maps via contiguity analysis Pages 847-854 Ludovic Lebart 16. Fast algorithm and implementation of dissimilarity self-organizing maps Pages 855-863 Brieuc Conan-Guez, Fabrice Rossi and Aïcha El Golli 17. Graph-based normalization and whitening for non-linear data analysis Pages 864-876 Catherine Aaron 18. Unfolding preprocessing for meaningful time series clustering Pages 877-888 Geoffroy Simon, John A. Lee and Michel Verleysen 19. Local multidimensional scaling Pages 889-899 Jarkko Venna and Samuel Kaski 20. Spherical self-organizing map using efficient indexed geodesic data structure Pages 900-910 Yingxin Wu and Masahiro Takatsuka 21. Advanced visualization of Self-Organizing Maps with vector fields Pages 911-922 Georg Pölzlbauer, Michael Dittenbach and Andreas Rauber 22. Online data visualization using the neural gas network Pages 923-934 Pablo A. Estévez and Cristián J. Figueroa 23. TreeSOM: Cluster analysis in the self-organizing map Pages 935-949 Elena V. Samsonova, Joost N. Kok and Ad P. IJzerman 24. Self-organizing neural networks to support the discovery of DNA-binding motifs Pages 950-962 Shaun Mahony, Panayiotis V. Benos, Terry J. Smith and Aaron Golden 25. A descriptive method to evaluate the number of regimes in a switching autoregressive model Pages 963-972 Madalina Oltean