Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function

This chapter presents a study on improving the quality of the self-organizing map (SOM). We have synthesized the relevant research on assessing and improving the quality of SOM in recent years, and then proposed a solution to improve the quality of the feature map by adjusting parameters of the Gaussian neighborhood function. We have used quantization error and topographical error to evaluate the quality of the obtained feature map. The experiment was conducted on 12 published datasets and compared the obtained results with some other improving neighborhood function methods. The proposed method received the feature map with better quality than other solutions

Building Adaptive Basis Functions with a Continuous Self-Organizing Map

This paper introduces CSOM, a continuous version of the Self-Organizing Map (SOM). The CSOM network generates maps similar to those created with the original SOM algorithm but, due to the continuous nature of the mapping, CSOM outperforms the SOM on function approximation tasks. CSOM integrates self-organization and smooth prediction into a single process. This is a departure from previous work that required two training phases, one to self-organize a map using the SOM algorithm, and another to learn a smooth approximation of a function. System performance is illustrated with three examples.Office of Naval Research (N00014-95-10409, N00014-95-0657

Building Adaptive Basis Functions with a Continuous SOM

This paper introduces CSOM, a distributed version of the Self-Organizing Map network capable of generating maps similar to those created with the original algorithm. Due to the continuous nature of the mapping, CSOM outperforms the traditional SOM algorithm in function approximation tasks. System performance is illustrated with three examples

Integration of geoelectric and geochemical data using Self-Organizing Maps (SOM) to characterize a landfill

Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases. For this reason, the delimitation and monitoring of the leachate plume are of significant importance. Geoelectric data (resistivity and IP), and surface methane measurements, are integrated and classified using an unsupervised Neural Network to identify possible risk zones in areas surrounding a landfill. The Neural Network used is a Kohonen type, which generates; as a result, Self-Organizing Classification Maps or SOM (Self-Organizing Map). Two graphic outputs were obtained from the training performed in which groups of neurons that presented a similar behaviour were selected. Contour maps corresponding to the location of these groups and the individual variables were generated to compare the classification obtained and the different anomalies associated with each of these variables. Two of the groups resulting from the classification are related to typical values of liquids percolated in the landfill for the parameters evaluated individually. In this way, a precise delimitation of the affected areas in the studied landfill was obtained, integrating the input variables via SOMs. The location of the study area is not detailed for confidentiality reasons.Comment: 11 pages, 7 figure

OctaSOM - An octagonal based SOM lattice structure for biomedical problems

In this study, an octagonal-based self-organizing network’s lattice structure is proposed to allow more exploration and exploitation in updating the weights for better mapping and classification performances.The neighborhood of the octagonal-based lattice structure provides more nodes for the weights updating than standard hexagonal-based lattice structure. Based on our experiment, the octagonal-based lattice structure performance is better than standard hexagonal lattice structure on biomedical datasets for classification problem. This indicates that proposed algorithm is an alternative lattice structure for self-organizing network which give more wisdom to classification problems especially in the biomedical domains