Exploratory Data Analysis Techniques Applied to Hydrological Time Series. I: Self-Organizing Maps

2008 ◽  
Author(s):  
Diego A Rivera ◽  
José L Arumí ◽  
Mario F Lillo
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Self Organizing Maps ◽  
Analysis Techniques ◽  
Exploratory Data ◽  
Self Organizing

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

scholarly journals Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map

2006 ◽  
Vol 3 (4) ◽  
pp. 1487-1516 ◽  
Author(s):  
L. Peeters ◽  
F. Bação ◽  
V. Lobo ◽  
A. Dassargues
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Three Dimensional ◽  
Spatial Knowledge ◽  
Self Organizing Map ◽  
Data Set ◽  
Hydrochemical Data ◽  
Som Algorithm ◽  
Exploratory Data ◽  
Self Organizing

Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 141 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. The standard SOM proves to be more adequate in representing the structure of the data set and to explore relationships between variables. The GEO3DSOM on the other hand performs better in creating spatially coherent groups based on the data.

scholarly journals Gender and Ultimatum in Pakistan: Revisited

2014 ◽  
Vol 53 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Saima Naeem ◽  
Asad Zaman
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Ultimatum Game ◽  
Original Data ◽  
Jel Classification ◽  
Data Set ◽  
Analysis Techniques ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

Razzaque (2009) studied the role of gender in the ultimatum game by running experiments on students in various cities in Pakistan. He used standard confirmatory data analysis techniques, which work well in familiar contexts, where relevant hypotheses of interest are known in advance. Our goal in this paper is to demonstrate that exploratory data analysis is much better suited to the study of experimental data where the goal is to discover patterns of interest. Our exploratory re-analysis of the original data set of Razzaque (2009) leads to several new insights. While we re-confirm the main finding of Razzaque regarding the greater generosity of males, additional analysis suggests that this is driven by student subculture in Pakistan, and would not generalise to the population at large. In addition, we find strong effect of urbanisation. Our exploratory data analysis also offers considerable additional insights into the learning process that takes place over the course of a sequence of games. JEL Classification: C78, C81, C91, J16 Keywords: Ultimatum Game, Gender Differences, Exploratory Data Analysis

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