scholarly journals A Complex MCDM Procedure for the Assessment of Economic Development of Units at Different Government Levels

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1067 ◽  
Author(s):  
Aleksandra Łuczak ◽  
Małgorzata Just
Keyword(s):  
Economic Development ◽  
Extreme Values ◽  
Standard Procedure ◽  
Value Theory ◽  
Multidimensional Data ◽  
Data Set ◽  
Development Level ◽  
Complex Procedure ◽  
Multidimensional Data Set ◽  
Different Levels

Studies on the economic development of government units are among the key challenges for authorities at different levels and an issue often investigated by economists. In spite of a considerable interest in the issue, there is no standard procedure for the assessment of economic development level of units at different levels of government (national, regional, sub-regional). This assessment needs a complex system of methods and techniques applicable to the various types of data. So, adequate methods must be used at each level. This paper proposes a complex procedure for a synthetic indicator. The units are assessed at different government levels. Each level (national, regional, and sub-regional) may be described with a particular type of variables. Set of data may include variables with a normal or near-normal distribution, a strong asymmetry or extreme values. The objective of this paper is to present the potential behind the application of a complex Multi-Criteria Decision Making (MCDM) procedure based on the tail selection method used in the Extreme Value Theory (EVT), i.e., Mean Excess Function (MEF) together with one of the most popular MCDM methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the economic development level of units at different government levels. MEF is helpful to identify extreme values of variables and limit their impact on the ranking of local administrative units (LAUs). TOPSIS is suitable in ranking units described with multidimensional data set. The study explored the use of two types of TOPSIS (classical and positional) depending on the type of variables. These approaches were used in the assessment of economic development level of LAUs at national, regional and sub-regional levels in Poland in 2017.

scholarly journals Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set

2019 ◽  
Vol 7 (2) ◽  
pp. 448 ◽  
Author(s):  
Saadaldeen Rashid Ahmed Ahmed ◽  
Israa Al Barazanchi ◽  
Zahraa A. Jaaz ◽  
Haider Rasheed Abdulshaheed
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithms ◽  
Gaussian Mixture ◽  
Multidimensional Data ◽  
Data Set ◽  
Multidimensional Data Set

scholarly journals Principal Graphs and Manifolds

2010 ◽  
pp. 28-59 ◽  
Author(s):  
Alexander N. Gorban ◽  
Andrei Y. Zinovyev
Keyword(s):  
Principal Component ◽  
Approximation Problem ◽  
Graph Grammar ◽  
Multidimensional Data ◽  
Data Set ◽  
Expectation Maximisation ◽  
The Family ◽  
Distance Approximation ◽  
Principal Points ◽  
Multidimensional Data Set

In many physical, statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpose, Karl Pearson invented principal component analysis in 1901 and found ‘lines and planes of closest fit to system of points’. The famous k-means algorithm solves the approximation problem too, but by finite sets instead of lines and planes. This chapter gives a brief practical introduction into the methods of construction of general principal objects (i.e., objects embedded in the ‘middle’ of the multidimensional data set). As a basis, the unifying framework of mean squared distance approximation of finite datasets is selected. Principal graphs and manifolds are constructed as generalisations of principal components and k-means principal points. For this purpose, the family of expectation/maximisation algorithms with nearest generalisations is presented. Construction of principal graphs with controlled complexity is based on the graph grammar approach.

scholarly journals 3D Visualization of Sound Fields Perceived by an Acoustic Camera

2015 ◽  
Vol 15 (7) ◽  
pp. 45-57
Author(s):  
Nevena Popova ◽  
Georgi Shishkov ◽  
Petia Koprinkova-Hristova ◽  
Kiril Alexiev
Keyword(s):  
3D Visualization ◽  
Multidimensional Data ◽  
Sound Waves ◽  
Signal Processing Algorithm ◽  
Data Set ◽  
3 Dimensional ◽  
Sound Fields ◽  
Neuro Fuzzy ◽  
Real Time Visualization ◽  
Multidimensional Data Set

Abstract The paper summarizes the application results of a recently proposed neuro-fuzzy algorithm for multi-dimensional data clustering to 3-Dimensional (3D) visualization of dynamically perceived sound waves recorded by an acoustic camera. The main focus in the present work is on the developed signal processing algorithm adapted to the specificity of multidimensional data set recorded by the acoustic camera, as well as on the created software package for real-time visualization of the “observed” sound waves propagation.

scholarly journals Variability and extremes: Statistical validation of the AWI-ESM

2021 ◽  
Author(s):  
Justus Contzen ◽  
Thorsten Dickhaus ◽  
Gerrit Lohmann
Keyword(s):  
General Circulation ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Climate Model ◽  
Extreme Values ◽  
Global Climate Model ◽  
General Circulation Models ◽  
Value Theory ◽  
Future Climate Change ◽  
Data Set

Abstract. Coupled general circulation models are of paramount importance to assess quantitatively the magnitude of future climate change. Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour. This is a problem because many severe consequences of climate changes are due to climate extremes. We present a method for model validation in terms of extreme values based on classical extreme value theory. We further discuss a clustering algorithm to detect spacial dependencies and tendencies for concurrent extremes. To illustrate these methods, we analyse precipitation extremes of the AWI-ESM global climate model compared to the reanalysis data set CRU TS4.04. The methods presented here can also be used for the comparison of model ensembles, and there may be further applications in palaeoclimatology.

scholarly journals A cognitive model for managing the national innovation system parameters based on international comparisons (the case of the EU countries)

2019 ◽  
Vol 17 (4) ◽  
pp. 153-162
Author(s):  
Igor Khanin ◽  
Gennadiy Shevchenko ◽  
Vladimir Bilozubenko ◽  
Maxim Korneyev
Keyword(s):  
Feature Vector ◽  
Cognitive Model ◽  
International Comparisons ◽  
Innovation System ◽  
Multidimensional Data ◽  
Data Set ◽  
Eu Countries ◽  
Global Innovation ◽  
Multidimensional Data Set ◽  
The Eu

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

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