The Often-Overlooked Power of Summary Statistics in Exploratory Data Analysis: Comparison of Pattern Recognition Entropy (PRE) to Other Summary Statistics and Introduction of Divided Spectrum-PRE (DS-PRE)

2021 ◽  
Vol 61 (9) ◽  
pp. 4173-4189
Author(s):  
Tahereh G. Avval ◽  
Behnam Moeini ◽  
Victoria Carver ◽  
Neal Fairley ◽  
Emily F. Smith ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Summary Statistics ◽  
Exploratory Data

Hyperbolic Space for Interactive Visualization

2011 ◽  
pp. 575-581
Author(s):  
Jörg Andreas Walter
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Hyperbolic Space ◽  
Exploratory Data Analysis ◽  
Strong Support ◽  
Interactive Visualization ◽  
User Interaction ◽  
Efficient Integration ◽  
Exploratory Data ◽  
The Given

For many tasks of exploratory data analysis, visualization plays an important role. It is a key for efficient integration of human expertise — not only to include his background knowledge, intuition and creativity, but also his powerful pattern recognition and processing capabilities. The design goals for an optimal user interaction strongly depend on the given visualization task, but they certainly include an easy and intuitive navigation with strong support for the user’s orientation.

scholarly journals Exploratory data analysis in the context of data mining and resampling.

2010 ◽  
Vol 3 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Chong Ho Yu
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Variable Selection ◽  
Statistical Modeling ◽  
Exploratory Data Analysis ◽  
Cluster Detection ◽  
New Methods ◽  
Exploratory Data

Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

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