In this paper, we discuss the use of Self Organizing Maps (SOM) for character and word clustering. The SOM is a particular kind of artificial neural network that computes an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning and SOM in the field of Document Image Analysis we describe our r...

SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data. In this paper, a new extraction method based on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks is proposed. Each SOM of the system is trained individually to provide best results for one class only. The experiments...

Clustering algorithms generally suffer from some well-known problems for which the Self Organizing Maps (SOM) algorithms are adept at handling. While there are many variants of the SOM algorithm, software programmes that implement the SOM algorithms have tended to give varying results even when tested on the same data sets. This can have serious implications when the goal of the clustering is n...

The aim of this article is to inquire about correlations between criminal phenomena and demographic factors. This international-level comparative study used a dataset covering 56 countries and 28 attributes. The data were processed with the Self-Organizing Map (SOM), assisted other clustering methods, and several statistical methods for obtaining comparable results. The article is an explorator...

One of the attractive features of Self-Organising Maps (SOM) is the so-called “topological preservation property”: observations that are close to each other in the input space (at least locally) remain close to each other in the SOM. In this work, we propose the use of a bootstrap scheme to construct a statistical significance test of the observed proximity among individuals in the SOM. While c...

The self-organizing map (SOM) is a method that represents statistical data sets in an ordered fashion, as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed. As a case study that instructs how to use the SOM to compare states of economic systems, the standard of living of different countries is analyzed using the SOM. Based on a...

The Self-Organizing Map (SOM) is one of the most popular neural network methods. It is a powerful tool in visualization and analysis of high-dimensional data in various engineering applications. The SOM maps the data on a two-dimensional grid which may be used as a base for various kinds of visual approaches for clustering, correlation and novelty detection. In this chapter, we present novel me...

This paper presents a new mutation operator called the Sobol Mutation (SOM) operator for enhancing the performance of Quantum Particle Swarm Optimization (QPSO) algorithm. The SOM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in th...

Although the SOM algorithm has been widely used with vectorial data, its principle is not restricted to metric vector spaces. Indeed, any set of items for which a similarity or pseudo-distance measure is available could be mapped onto the SOM grid in an ordered fashion. As Kohonen and Somervuo (2002) pointed out, the optimal speed of shrinking of the neighbourhood range function on nonvectorial...

The Self-Organizing Map (SOM ) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper we describe Fast Learning SOM (FLSOM ) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that ...