scholarly journals What Do Global Metrics Tell Us about the World?

2019 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Global Indicators ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global indicators that measure different economic, political and social attributes of countries in the world. This paper seeks to answer two questions. First, what is the degree of overlap between these different measures? Are they, in fact, measuring the same underlying dimension? To answer this question, we employ a principal component analysis (PCA) to 15 indices across 145 countries. The results demonstrate that there is one underlying dimension that combines economic development and social progress with state stability. Second, how do countries score on this dimension? The results of the PCA allow us to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as rich, poor and middle countries. A five-group classification provided a more nuanced categorization described as: The very rich, free and stable; affluent and free; upper middle; lower middle; poor and not free.

scholarly journals What Do Global Metrics Tell Us About The World?

2018 ◽  
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Economic Development ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Components Analysis ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global metrics. To find the degree of overlap between these different measures, we employ a principal components analysis (PCA) to 15 indices across 145 countries. Our results demonstrate that the most important underlying dimension highlights that economic development and social progress go hand in hand with state stability. The results are used to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as Rich, Poor and Middle countries. A five-group classification provided a more nuanced categorization described as; The Very Rich, Free and Stable, Affluent and Free, Upper Middle, Lower Middle, and Poor and Not Free.

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

Research on the Coordination of Economy and Environment for Poyang Lake Ecological Economic Zone

2013 ◽  
Vol 726-731 ◽  
pp. 3976-3980
Author(s):  
Chuan Kui Liu ◽  
You Yuan Wang
Keyword(s):  
Principal Component Analysis ◽  
Sustainable Development ◽  
Economic Development ◽  
Environmental Protection ◽  
Quantitative Method ◽  
Poyang Lake ◽  
Principal Component ◽  
Component Analysis ◽  
Coordinated Development ◽  
Ecological Economic

It is a very important problem that economic and environmental coordinated development in PLEEZ. This paper found economic and environmental systems, a quantitative method was proposed which systemic coordinated evaluation based on the principal component analysis and system index of sustainable development for PLEEZ, the conclusions provide a useful reference to economic development and environmental protection coordination of PLEEZ.

Principal Component Analysis the Economy of Shanghai in 2010

2011 ◽  
Vol 50-51 ◽  
pp. 404-408
Author(s):  
Xiao Qiang Guo ◽  
Zhen Dong Li ◽  
Dong Dong Hao ◽  
Xin Xie ◽  
Jian Min Wang
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Short Term ◽  
Construction Costs ◽  
World Expo ◽  
Shanghai World Expo

This paper from the economic analysis, quantitative evaluation of the 2010 Shanghai World Exop impact. First, from the short-term and long-term benefits of the two considerations, the loss of earnings, base construction costs on the percentage of total funding, permanent building retained, the number of daily tours, the number of participating countries for the evaluation index, subjectively weight to the five indicators，calculate its scores to rank for five World Expos including Shanghai World Expo. Second, using principal component analysis, we get the five indicators of objective weighting and ranking for above five World Expos. The results show that the Shanghai World Expo will boost the economic development and has a huge influence on the economy

scholarly journals Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier

2020 ◽  
Vol 13 (2) ◽  
pp. 11
Author(s):  
Bekti Endar Susilowati ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Large Area ◽  
Robust Principal Component Analysis ◽  
Data Set ◽  
Silhouette Width ◽  
The World

Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster. 

scholarly journals Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020 ◽  
Author(s):  
Edy Irwansyah ◽  
Ebiet Salim Pratama ◽  
Margaretha Ohyver
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Principal Component Analysis ◽  
Data Reduction ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Techniques ◽  
The World ◽  
Using Data

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

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