The present study uses the Kohonen self organizing map (SOM) to represent the popularity patterns of Myspace music artists from their attributes on the platform and their position in the social network. The method is applied to cluster the profiles (the nodes of the social network) and the best friendship links (the edges). It shows that the SOM is an efficient tool to interpret the complex lin...

This paper presents an approach to the well-known Traveling Salesman Problem (TSP) via competitive neural networks. The neural network model adopted in this work is the Kohonen Network or Self-Organizing Maps (SOM), which has important topological information about its neurons configuration. This paper is concerned with the initialization aspects, parameters adaptation, and the complexity analy...

In this study self-organizing maps (SOM) were utilized for spatiotemporal analysis and classification of body surface potential mapping (BSPM) data. Altogether 86 cardiac depolarization (QRS) sequences paced by a catheter in 18 patients were included. Spatial BSPM distributions at every 5 ms over the QRS complex were first presented to an untrained SOM. The learning process of the SOM units org...

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SO...

Computer network is one of the world's most important infrastructures in twenty-first Century, network fault diagnosis has become the focus of attention. With the development of artificial intelligence, using the neural network technology into the network fault diagnosis area can play an important role to the advantages of neural network in fault diagnosis. In this paper, the method is widely u...

In this paper, neuro based intelligent diagnosis methods for electro-mechanical control system are proposed. A self organizing map neural network (SOM) is used to classify measured data of the target system as a qualitative diagnostic method. Besides of the above procedure, it is expected to attain more efficient maintenance by a quantitative estimation of failure. For the purpose, new method i...

Remote sensing has changed modern oceanography by proving synoptic periodic data which can be processed. Since the satellite data are usually too much and nonlinear, in most cases, it is difficult to distinguish the patterns from these images. In fact, SOM (Self-Organizing Maps) model is a type of ANN (Artificial Neural Network) that has the ability to distinguish the efficient patterns from th...

With increases in the amounts of available DNA sequence data, it has become increasingly important to develop tools for comprehensive systematic analysis and comparison of species-specific characteristics of protein-coding sequences for a wide variety of genomes. In the present study, we used a novel neural-network algorithm, a self-organizing map (SOM), to efficiently and comprehensively analy...

Two neural network approaches, Kohonen's self-organizing (feature) map (SOM) and the topology representing network (TRN) of Martinetz and Schulten are employed in the context of competitive market structuring and segmentation analysis. In an empirical study using brands preferences derived from household panel data, we compare the SOM and TRN approach to MULTICLUS, a parametric latent vector mu...

The Self-Organizing Map (SOM) is an artificial neural network model based on unsupervised learning. In this paper, the use of the SOM in natural language processing is considered. The main emphasis is on natural features of natural language including contextuality of interpretation, and the communicative and social aspects of natural language learning and usage. The SOM is introduced as a gener...