The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning effi...

− The concept of similarity is important for many data mining related applications such as content-based music retrieval. Defining similarity can be very difficult if several aspects are involved. For example, music similarity depends on the melody, rhythm, or instruments. The Self-Organizing Map is a powerful tool to visualize how the data looks like from a certain perspective of similarity. I...

Self-organised maps (SOM) have been widely used for cluster analysis and visualisation purposes in exploratory data mining. In image retrieval applications, SOMs have been used to visualise high-dimensional feature space and build indexing structures. In this paper, we extend the use of SOMs for profiling and comparison of image collections, and present empirical results obtained in collection ...

Self-organizing maps (SOMs) and other artificial intelligence approaches developed by Kohonen can be used to model solve environmental challenges. To emphasize the classification of Physico-chemical parameters Inaouen watershed, we presented a strategy based on self-organizing topological map (SOM) neural network in this study. The use classify samples resulted following five categories: Low qu...

A new multi-dimensional interpolation method for function approximation using Self-Organizing Maps (SOMs) [1] is proposed. The output is continuous and infinitely differentiable in all the interpolation area, and the error function has no local minima insuring the convergence of the learning toward the minimal error. The complexity is O(n) in the general case (where n is the number of neurons)....

An art installation was on display in the Centre Pompidou National Museum of Modern Art in Paris, were visitors could contribute with their own personal objects, adding keyword descriptions and quantified semantic features such as age or hardness. The data was projected in real-time onto a Self-Organizing Map (SOM) and shown in the gallery. In this paper we analyze the same data by extracting v...

Self-organising Maps (SOMs) are a very useful method for exploring and analysing large data collections: They project high-dimensional data into a low-dimensional output space so that it is easier to analyse for humans than the original data. For the purpose of analysis, plenty of visualisations exist which display different aspects and properties of the maps and the data. There are, however, v...

The Self-Organizing Map (SOM) is one of the popular Artificial Neural Networks which is a useful in clustering and visualizing complex high dimensional data. Conventional SOMs are based on the two-dimensional (2D) grid structure, which usually results in less accurate representation of the data. Several SOMs using spherical data structures have been proposed to remove the “border effect”. In th...

market crash is a phenomenon which occurs in stock markets occasionally and leads to loss of the investors’ wealth and assets in a relatively short period of time. therefore, attempts for prediction of this phenomenon are of much importance for the investors, financial institutions and government. to this date, numerous and varied studies have been carried out for predicting and modeling  stock...

Engineers tasked with designing large and complex systems are continually in need of decision-making aids able to sift through enormous amounts of data produced through simulation and experimentation. Understanding these systems often requires visualizing multidimensional design data. Visual cues such as size, color, and symbols are often used to denote specific variables (dimensions) as well a...