Databases Reference
In-Depth Information
3
Advanced Topics in Initial
Exploration and Dataset
Preparation Using VisMiner
In Chapter 2, as part of an initial exploration, most of the viewers for data
visualization were introduced. At this time, the correlation matrix and the
parallel plot were also used to create data subsets. The correlation matrix
allowed us to project attributes (dimension reduction) from a dataset, while the
parallel plot allowed us to both project attributes and filter observations.
In Chapter 3, although the location plot viewer is introduced, we primarily
present additional functionality for dataset preparation. Specifically, we use
VisMiner to:
handle missing values
create computed columns
aggregate observations
merge datasets
detect and eliminate outliers.
Missing Values
When working with “real world” data, a common problem is that of missing
data. Most analysis algorithms require a complete set of data in order to conduct
the analysis. VisMiner is no exception. It requires that all missing values be
