[1] These techniques involve looking at datasets through different, correlated views and iteratively selecting and examining features the user finds interesting.
The objective of IVA is to gain knowledge which is not readily apparent from a dataset, typically in tabular form.
This can involve generating, testing or verifying hypotheses, or simply exploring the dataset to look for correlations between different variables.
D. projects have explored the concept since then - notably Helmut Doleisch in 2004,[5] Johannes Kehrer in 2011 [6] and Zoltan Konyha in 2013.
[8] The objective of Interactive Visual Analysis is to discover information in data which is not readily apparent.
The most basic form of IVA is to use coordinated multiple views [9] displaying different columns of our dataset.
Discoveries made after brushing of the data and looking at the linked views can be used as a starting point for repeating the process, leading to a form of information drill-down.
The independent variables represent the domain of the observed values, such as for instance time and space.
For datasets where the relationships between the variables are reasonably simple, this technique is usually sufficient for the user to achieve the required level of understanding.
[7] A simple example would be the analysis of weather data: The analyst might want to discover regions that both have warm temperatures and low precipitation.
Advanced brushing generates a faster response than attribute derivation, but has a higher learning curve and require a deeper understanding of the dataset.
After detection of higher-order features, the calculated attributes would be connected to the original data set and subjected to the normal technique of linking and brushing.
Examples from a meteorological dataset would be which regions have a warm climate or which times of the year have a lot of precipitation.
In the case of meteorological data, we could for instance discover the temperature distribution during the winter months.
Since each of the linked views usually has two or more dimensions, multivariate analysis can implicitly uncover higher-dimensional features of the data which would not be readily apparent from e.g. a simple scatterplot.