Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions fro...

Advances in technology are turning our mobile phones or PDAs into powerful computing devices capable of executing data mining algorithms. This paper discusses how the Self Organizing Map (SOM) algorithm can be adapted to a cooperating network of these devices. This approach opens the door to several new applications of data mining, including active data-collecting devices and ondemand knowledge...

This paper sheds some light on the claim that Emergent SOM (ESOM) are different from other SOM. The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The most critical of the postulates are those concerned with “the whole is more than the sum of its parts”...

A new algorithm for generating shape models using a Self-Organizing map (SOM) is presented. The aim of the model is to develop an approach for shape representation and classification to detect differences in the shape of anatomical structures. The Self-Organizing map requires specification of the number of clusters in advance, but does not depend upon the choice of an initial contour. This tech...

Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. Artificial Neural Networks are relatively ...

In this article, we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map. Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress.

© Tag Clustering with Self Organizing Maps Marco Luca Sbodio, Edwin Simpson HP Laboratories HPL-2009-338 SOM, clustering, machine learning, folksonomy, tagging, web 2.0 Today, user-generated tags are a common way of navigating and organizing collections of resources. However, their value is limited by a lack of explicit semantics and differing use of tags between users. Clustering techniques th...

Self-Organizing Maps (SOMs) are often visualized by applying Ultsch’s Unified Distance Matrix (U-Matrix) and labeling the cells of the 2-D grid with training data observations. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining applications: (a) While the U-Matrix indicates the locat...

A topology-selection method for self-organizing maps (SOMs) based on empirical Bayesian inference is presented. This method is natural extension of the hyperparameter-selection method presented earlier, in which the SOM algorithm is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the centroid parameters, and optimal hyperparameters are obtaine...

This paper approaches the problem of image denoising from an Independent Component Analysis (ICA) perspective. Considering that the pixels intensity constituting the crude images represents the useful signal corrupted with noise, we show that, a nonlinear ICA-based approach can provide a satisfactory solution to the NonLinear Blind Source Separation problem (NLBSS). SelfOrganizing Maps (SOMs) a...