Gintautas Dzemyda

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This paper deals with a method, called locally linear embedding. It is a nonlinear dimensionality reduction technique that computes low-dimensional, neighbourhood preserving embeddings of high dimensional data and attempts to discover nonlinear structure in high dimensional data. The implementation of the algorithm is fairly straightforward, as the(More)
Most of real-life data are not often truly high-dimensional. The data points just lie on a low-dimensional manifold embedded in a high-dimensional space. Nonlinear manifold learning methods automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space,(More)
In this paper, we present an approach of the Web application (as a service) for data mining oriented to the multidimensional data visualization. The stress is put on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation includes five visualization methods: MDS SMACOF algorithm,(More)
In this paper, the relative multidimensional scaling method is investigated. This method is designated to visualize large multidimensional data. The method encompasses application of multidimensional scaling (MDS) to the so-called basic vector set and further mapping of the remaining vectors from the analyzed data set. In the original algorithm of relative(More)
The analysis of the method for multiple criteria optimization problems applying a computer network has been presented in the paper. The essence of the proposed method is the distribution of the concrete optimization problem into the network rather than the parallelization of some optimization method. The aim of the authors is to design and investigate the(More)
In 2003, a health IT programme for clinical decision support started in Lithuania. An initial goal was to create databases for ophthalmology images and to develop processing algorithms to extract diagnostically valuable information from images. We have investigated how vectors, consisting of the parameters derived from fundus images, are distributed and(More)
The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural(More)
Visual data mining is an efficient way to involve human in search for a optimal decision. This paper focuses on the optimization of the visual presentation of multidimensional data. A variety of methods for projection of multidimensional data on the plane have been developed. At present, a tendency of their joint use is observed. In this paper, two(More)
The paper deals with the analysis of Research and Technology Development (RTD) in the Central European countries and the relation of RTD with economic and social parameters of countries in this region. A methodology has been developed for quantitative and qualitative ranking and estimates of relationship among multidimensional objects on the base of such(More)
In the paper, we analyze the software that realizes the self-organizing maps: SOM-PAK, SOM-TOOLBOX, Viscovery SOMine, Nenet, and two academic systems. Most of the software may be found in the Internet. These are freeware, shareware or demo. The self-organizing maps assist in data clustering and analyzing data similarities. The software differs one from(More)