Exploratory Data Analysis Task

An Exploratory Data Analysis Task is an exploratory study task that is a data analysis task (aiming to formulate hypotheses).

• AKA: Exploratory Data Analysis.
• Context:
  • It can be performed during an Exploration Phase (as an Exploratory Data Analysis Activity).
• Example(s):
  • a Cluster Analysis/Cluster Mining Task.
  • an Association Rule Mining Task.
  • an Exploratory Factor Analysis.
  • an Online Analytical Processing Task.
  • It can be supported by a Visualization Task.
  • …
• Counter-Example(s):
  • a Predictive Modeling Task.
  • a Confirmatory Data Analysis Task.
  • a Patient Record Analysis Task.
• See: Knowledge Discovery Task, Exploratory, Abductive Reasoning, Stem-and-leaf plot, Odds ratio, Multidimensional scaling.

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References

2011

• (Wikipedia, 2011) ⇒ http://en.wikipedia.org/wiki/Exploratory_data_analysis
  • QUOTE: In statistics, exploratory data analysis (EDA) is an approach to analysing data sets to summarize their main characteristics in easy-to-understand form, often with visual graphs, without using a statistical model or having formulated a hypothesis. Exploratory data analysis was promoted by John Tukey to encourage statisticians visually to examine their data sets, to formulate hypotheses that could be tested on new data-sets. …

    … Typical graphical techniques used in EDA are: Box plot; Histogram; Multi-vari chart; Run chart; Pareto chart; Scatter plot; Stem-and-leaf plot; Odds ratio; Chi-square; Multidimensional scaling; and Targeted Projection Pursuit.

    Typical quantitative techniques are: Median polish; the Trimean; Letter values; Resistant line; Resistant smooth; Rootogram; Ordination

2009

• http://www.itl.nist.gov/div898/handbook/eda/eda.htm
• http://www.itl.nist.gov/div898/handbook/eda/section1/eda11.htm
  • Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
    • 1. maximize insight into a data set;
    • 2. uncover underlying structure;
    • 3. extract important variables;
    • 4. detect outliers and anomalies;
    • 5. test underlying assumptions;
    • 6. develop parsimonious models; and
    • 7. determine optimal factor settings.
  • Focus: The EDA approach is precisely that--an approach--not a set of techniques, but an attitude/philosophy about how a data analysis should be carried out.

2000

• (Anselin et al., 2000) ⇒ Luc Anselin, Jacqueline Cohen, David Cook, Wilpen Gorr, and George Tita. (2000). “Spatial Analyses of Crime.” In: Criminal justice, 4(2).
  • QUOTE: … Special attention is given to some practical and accessible methods of exploratory data analysis that arguably should be the starting place of any empirical analyses of the relationship of place to crime.

1999

• (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
  • QUOTE: Exploratory Data Analysis: The use of graphical and descriptive statistical techniques to learn about the structure of a dataset.

1977

• (Tukey, 1977) ⇒ John W. Tukey. (1977). “Exploratory Data Analysis. Addison-Wesley. ISBN:0201076160

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