The Self-Organizing Map (SOM) is a popular and well-studied unsupervised learning technique. Much work has been done recently on visualizing the results of the SOM algorithm, using static non-interactive approaches. This paper presents two new SOM visualization methods, based on the grand tour and linked brushing. These new methods use animation to show progress of the algorithm in the input sp...

A HARDWARE IMPLEMENTATION OF THE SOM FOR A NETWORK INTRUSION DETECTION SYSTEM Brent W. Roeder, B.S. Virginia Tech, 2005 Thesis Director: Dr. Kris Gaj This thesis describes the research and development of a hardware implementation of the self organizing map (SOM) for a network intrusion detection system. As part of the thesis research, Kohonen’s SOM algorithm was examined and different hardware ...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A theoretical and practical challenge in the SOM has been the diffi­ culty to treat the method as a statistical model fitting procedure. In this chapter we give a short review of statistical approaches for the SOM. Then we present the probability density model for which the SOM training gives the maximum likeli­ h...

A SpectrophoreTM is a one-dimensional descriptor that describes the three-dimensional molecular field surrounding a molecule generated by a given set of atomic properties. In a typical application, SpectrophoresTM are calculated from the molecular shape in combination with the electrostatic, lipophilic, softness, and hardness potential surrounding the molecules. Given that molecules with simila...

The Self-Organizing Map (SOM) enjoys significant popularity in the field of data mining and visualization. While its topology-preserving mapping allows easier interpretation of complex data, communicating the location of clusters and individual data items as well as memorizing locations are not solved satisfactorily in conventional rectangular maps. In this paper, a variant of self-organizing m...

Two well-known variants of the self-organizing map (SOM) that are based on order statistics are the marginal median SOM and the vector median SOM. In the past, their efficiency was demonstrated for color image quantization. In this paper, we employ the well-known IRIS data set and we assess their performance with respect to the accuracy, the average over all neurons mean squared error between t...

Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achi...