The visual display of quantitative information
The visual display of quantitative information
Envisioning information
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Efficient approaches for materialized views selection in a data warehouse
Information Sciences: an International Journal
Summarizing data using a similarity based mountain method
Information Sciences: an International Journal
Information Sciences: an International Journal
The application of the Hilbert spectrum to the analysis of electromyographic signals
Information Sciences: an International Journal
Multiple correspondence analysis for "tall" data sets
Intelligent Data Analysis
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Empirical studies of human systems often involve recording multidimensional signals because the system components may require physical measurements (e.g., temperature, pressure, body movements and/or movements in the environment) and physiological measurements (e.g., electromyography or electrocardiography). Analysis of such data becomes complex if both the multifactor aspect and the multivariate aspect are retained. Three examples are used to illustrate the role of fuzzy space windowing and the large number of data analysis paths. The first example is a classic simulated data set found in the literature, which we use to compare several data analysis paths generated with principal component analysis and multiple correspondence analysis with crisp and fuzzy windowing. The second example involves eye-tracking data based on advertising, with a focus on the case of one category variable, but with the possibility of several space windowing models and time entities. The third example concerns car and head movement data from a driving vigilance study, with a focus on the case involving several quantitative variables. The notions of analysis path multiplicity and information are discussed both from a general perspective and in terms of our two real examples.