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[Multiple visualisation in data analysis: a ViSta application for principal component analysis]
Rubén Ledesma1, J G Molina, Forrest W Young
1CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas), Argentina.
Psicothema
|July 10, 2007
Summary
Multiple visualisation (MV) offers unique data analysis features but is underutilized. This paper details MV application, design rules, development guidelines, and implementation in ViSta, including a principal component analysis example.
Area of Science:
- Statistics
- Data Visualization
- Computer Science
Context:
- The application of statistical graphical methods in data analysis is limited.
- Multiple visualisation (MV) offers unique features for data exploration and analysis.
- Existing statistical software may not fully support advanced visualisation techniques.
Purpose:
- To describe the application of the multiple visualisation (MV) graphical method.
- To present design rules and a general development framework for MVs.
- To illustrate MV implementation using ViSta and a principal component analysis example.
Summary:
- This paper introduces multiple visualisation (MV), a statistical graphical method with underutilized potential.
- It outlines MV application, design principles, and a development framework, demonstrating implementation in the ViSta statistical system.
- An example focusing on principal component analysis showcases MV's utility, alongside a discussion of its limitations.
Impact:
- Enhances data analysis practices by introducing a powerful, yet underused, visualisation technique.
- Provides practical guidelines and a framework for developing and implementing MVs.
- Facilitates deeper insights from complex datasets through advanced graphical representation, particularly in areas like principal component analysis.
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