Advances in Self Organising Maps

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This study aimed a procedure of unsupervised classification for remote sensing images based on a combination of Self-Organizing maps (SOM) and segmentation. The integration is conceived first obtaining clusters of the spectral behavior of the satellite image using Self-Organizing Maps. As visualization technique for the SOM is used the U-matrix. Subsequently is used seeded region growing segmentation technique to obtain a delimitation of the clusters in the data. Finally, from the regions of neurons in the U-matrix are deduced the clusters in the original pixels of the image. To evaluate the proposed methodology it was considered a subset of a satellite image as use case. The results were measured through accuracy assessment of the case and comparing definition of the obtained clusters against each technique separately. Cramers'V was used to evaluate the association between clustering obtained each method separately and reference data for the specific use case

Pendekatan self-organizing maps dalam data mining untuk clustering perubahan harga saham=Seft organizing maps approach in data mining for clustering the ...

ABSTRACT Money market is hoped can collect society\u27s moneys for building and increasing society\u27s income. Society as the main investor on money market has to know and understand the analysis of stock infestation for knowing how much of it, so they can give the most optimal return. One of the approaches for evaluating stock infestation is technical analysis that used data or note of market, which is published. For examples stock cost, market volume, the index of consolidations stock or individual, and the other factor which have technical characters. The purpose of this research are for making a system which based to SOM for knowing what day the prices of stock are highest or lowest based on the frequent of each day appear and for knowing which algorithm is the most objective. This research use undirected data mining method that is clustering. Self-Organizing Maps (SOM) with training algorithm sequential and batch are used for clustering with output as like as clustering visualization. The result of this research show that the highest of stock prices is on Friday and the lowest is on Wednesday and Thursday. Key words : Data mining, clustering, Self-Organizing Maps

Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-319-46681-1_17Versión final aceptada de: Álvarez, M.A., Dafonte, C., Garabato, D., Manteiga, M. (2016). Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_17[Abstract]: A billion stars: this is the approximate amount of visible objects estimated to be observed by the Gaia satellite, representing roughly 1 % of the objects in the Galaxy. It constitutes the biggest amount of data gathered to date: by the end of the mission, the data archive will exceed 1 Petabyte. Now, in order to process this data, the Gaia mission conceived the Data Processing and Analysis Consortium, which will apply data mining techniques such as Self-Organizing Maps. This paper shows a useful technique for source clustering, focusing on the development of an advanced visualization tool based on this technique

The Hydrothermal System of Solfatara Crater (Campi Flegrei, Italy) Inferred From Machine Learning Algorithms

Two machine learning algorithms were applied to three multivariate datasets acquired at Solfatara volcano. Our aim was to find an unbiased and coherent synthesis among the large amount of data acquired within the crater and along two orthogonal vertical NNE- and WNW-trending cross-sections. The first algorithm includes a new approach for a soft K-means clustering based on the use of the silhouette index to control the color palette of the clusters. The second algorithm which uses the self-organizing maps incorporates an alternative method for choosing the number of nodes of the neural network which aims to avoid the need for downstream clustering of the results of the classification. Both methods achieved an objective characterization of the shallow hydrothermal system of the volcano, enhancing and highlighting subtle geophysical anomalies likely correlated to structural pathways of deep magmatic degassing. Comparison between the results of K-means and self-organizing maps on the datasets with the largest number of nodes confirms that, with respect to the K-means, self-organizing maps compress the data in a way that better highlights finer details of the original data. However, the choice of the coloring scheme of the neurons is critical for an effective visualization of the results. Unsupervised integration of the three multivariate datasets allowed us to spatially correlate, with a high-degree of confidence, the geophysical anomalies recorded at the surface of the crater with those recorded at the subsurface along the two cross-sections. it also allowed us to associate those anomalies to different hydrothermal features such as shallow gas-saturated and water-saturated zones and their underlying fractures/faults feeding system. Our results suggest that the main shallow structural patterns, which influence the hydrothermal dynamics at Solfatara volcano, remained substantially unchanged in the last fifteen years. Our approach shows that the use of clustering methods to interpret multivariate data reduces interpretation uncertainties and achieves an improved understanding of the complex dynamics occurring in volcanoes

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators