scholarly journals STRATEGIES OF SELECTING THE BASIS VECTOR SET IN THE RELATIVE MDS

2006 ◽  
Vol 12 (4) ◽  
pp. 283-288
Author(s):  
Jolita Bernatavičienė ◽  
Gintautas Dzemyda ◽  
Olga Kurasova ◽  
Virginijus Marcinkevičius
Keyword(s):  
Experimental Investigation ◽  
Data Visualization ◽  
Basis Vector ◽  
Multidimensional Data ◽  
Clustering Method ◽  
Data Set ◽  
Original Algorithm ◽  
Multidimensional Data Visualization ◽  
Visualization Process ◽  
Basis Vectors

In this paper, a method of large multidimensional data visualization that associates the multidimensional scaling (MDS) with clustering is modified and investigated. In the original algorithm, the visualization process is divided into three steps: the basis vector set is constructed using the k‐means clustering method; this set is projected onto the plane using the MDS algorithm; the remaining data set is visualized using the relative MDS algorithm. We propose a modification which differs from the original algorithm in the strategy of selecting the basis vectors. In our modification, the set of basis vectors consists of vectors that are selected from k clusters in a new way. The experimental investigation showed that the modification exceeds the original algorithm in visualization quality and computational expenses.

scholarly journals WEB APPLICATION FOR LARGE-SCALE MULTIDIMENSIONAL DATA VISUALIZATION

2011 ◽  
Vol 16 (1) ◽  
pp. 273-285 ◽  
Author(s):  
Gintautas Dzemyda ◽  
Virginijus Marcinkevičius ◽  
Viktor Medvedev
Keyword(s):  
Data Mining ◽  
Data Visualization ◽  
Web Application ◽  
Large Scale ◽  
Visual Presentation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Set ◽  
Multidimensional Data Visualization ◽  
Multidimensional Data Set

In this paper, we present an approach of the web application (as a service) for data mining oriented to the multidimensional data visualization. This paper focuses on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation of such a web application obtains a multidimensional data set and as a result produces a visualization of this data set. It also supports different configuration parameters of the data mining methods used. Parallel computation has been used in the proposed implementation to run the algorithms simultaneously on different computers.

scholarly journals Multidimensional data visualization

2002 ◽  
Vol 34 (2) ◽  
pp. 158-162 ◽  
Author(s):  
Matthew J. Pastizzo ◽  
Robert F. Erbacher ◽  
Laurie B. Feldman
Keyword(s):  
Data Visualization ◽  
Multidimensional Data ◽  
Multidimensional Data Visualization

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

Application of Multidimensional Data Visualization by Means of Self-Organizing Kohonen Maps to Evaluate Classification Possibilities of Various Coal Types / Zastosowanie Wizualizacji Wielowymiarowych Danych Za Pomocą Sieci Kohonena Do Oceny Możliwości Klasyfikacji Różnych Typów Węgla

2015 ◽  
Vol 60 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Dariusz Jamróz ◽  
Tomasz Niedoba
Keyword(s):  
Data Visualization ◽  
Multidimensional Data ◽  
Material Quality ◽  
Kohonen Maps ◽  
Multidimensional Data Visualization ◽  
Visualization Techniques ◽  
Modern Tool ◽  
Coal Type

Abstract Multidimensional data visualization methods are a modern tool allowing to classify some analysed objects. In the case of grained materials e.g. coal, many characteristics have an influence on the material quality. The paper presents the possibility of applying visualization techniques for coal type identification and determination of significant differences between various types of coal. To achieve this purpose, the method of Kohonen maps was applied by means of which three types of coal - 31, 34.2 and 35 (according to Polish classification of coal types) were investigated. It was stated that the applied methodology allows to identify certain coal types efficiently and can be used as a qualitative criterion for grained materials.

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