Handle is an important property of fabrics. In this work we tried to predict the handles of some worsted fabrics by their physical properties using a backpropagation network. Also an unsupervised kohonen network was used for clustering the fabrics. Physical properties of fabrics were measured by universal test equipments and hand values of the fabrics were determined by a panel of judges consis...

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characterist...

The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -k-means ...

This article is an extended version of a paper presented in the WSOM’2012 conference [1]. We display a combination of factorial projections, SOM algorithm and graph techniques applied to a text mining problem. The corpus contains 8 medieval manuscripts which were used to teach arithmetic techniques to merchants. Among the techniques for Data Analysis, those used for Lexicometry (such as Factori...

Deterministic nonlinear prediction is a pow erful tec hnique for the analysis and prediction of time series generated by nonlinear dynamical systems. In this paper the use of a Kohonen netw ork asa component of one deterministic nonlinear prediction algorithm is suggested. In order to evaluate the performance of the proposed algorithm, it was applied to the prediction of time series generated b...

The Kohonen self-organizing map (SOM) is an unsupervised neural network with a competitive learning strategy that uses a neighborhood lateral interaction function to discover the hidden topological structure of the input data and has both visualization and clustering properties. In this presentation, we propose batch SOM algorithms with automatic weighting of the variables to training the Kohon...

Many of the properties of the well-known Kohonen map algorithm are not easily derivable from its discrete formulation. For instance, the “projection” implemented by the map from a high dimensional input space to a lower dimensional map space must be properly regarded as a projection from a smooth manifold to a lattice and, in this framework, some of its property are not easily identified. This ...

Exploratory data mining using artificial neural networks offers an alternative dimension to data mining, in particular techniques geared towards data clustering and classification. In this paper, we argue the case for using neural networks as a viable data mining tool that can provide statistical insights and models from large data-sets. We demonstrate how Self-Organizing Kohonen Maps, an unsup...

The paper presents the application of the hybrid neural network to the solution of the calibration problem of the solid state sensor array used for the gas analysis. The applied neural network is composed of two parts: the selforganizing Kohonen layer and multilayer perceptron (MLP). The role of the Kohonen layer is to perform the feature extraction of the data and MLP network fulfills role of ...

Here a new algorithm based on the Self Organizing Map is presented which allows for easier categorization of textual data. The algorithm is based heavily on the concept of metadata and its extraction and providing an abstract look at that data. The algorithm is titled the Abstract Self Organizing Map for that reason. The paper discusses the current and potential impact of metadata, the current ...