Marriage of fuzzy sets and multiple correspondence analysis: Examples with subjective interval data and biomedical signals
✍ Scribed by P. Loslever; S. Bouilland
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 673 KB
- Volume
- 107
- Category
- Article
- ISSN
- 0165-0114
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✦ Synopsis
The multiple correspondence analysis (MCA) is a descriptive and multidimensional method which can investigate several empirical situations, each situation being described by a categorical variable set. This paper shows how the fuzzy sets principle can be used to transform raw continuous data into categorical. The transformation is considered in two main stages: data characterizing and data coding. The data characterizing is performed to build analysis variables from complex empirical variables, such as multidimensional signals, using fuzzy windowing of the time and/or the space axes. The analysis variables are indicators summarizing the information within the so obtained windows. The data coding is performed to build homogeneous analysis variables, i.e. variables that are based on a qualitative scale using fuzzy windowing. Both methodological and practical aspect are considered in this paper through two examples. The "rst example considers the comparative analysis of force and force derivative signals in several load lifting conditions. The second example considers the analysis and the modelling of individual agreements between a graphical view showing two bargraphs and the assertion the height of the ,rst bar is large and the height of the second is large, the agreement being given through an interval.