A scale fuzzy windowing comparison applied to multivariate descriptive analysis
| Autor: | Laurent Cauffriez, R. Copin, F. Turgis, Pierre Loslever, N. Caouder |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer science
business.industry Inference Experimental data Machine learning computer.software_genre Fuzzy logic Correspondence analysis Theoretical Computer Science Data set Artificial Intelligence Multiple correspondence analysis Relationship square Principal component analysis Computer Vision and Pattern Recognition Artificial intelligence Data mining business computer |
| Zdroj: | Intelligent Data Analysis. 16:279-303 |
| ISSN: | 1571-4128 1088-467X |
| DOI: | 10.3233/ida-2012-0524 |
| Popis: | Observational and experimental data are often investigated into so that the factor effects and/or variables connections can be assessed quickly and easily via inference tests. This article suggests starting the statistical analysis using a 5-step descriptive procedure: 1 Data characterization, 2 Data coding, 3 Data table drafting, 4 Data table analysis and 5 Result presentation. In order to illustrate this preliminary statistical analysis, two data set examples are considered --one from a small simulated system and one from a large mechatronic system--using two different methods: Principal Component Analysis with usual statistical summaries and Multiple Correspondence Analysis with indicators obtained through fuzzy space windowing. In an Intelligent Data Analysis context, the discussion weighs out the pros and the cons of these approaches, prior to using procedures 5-step inference procedures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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