  The processing of data which contain missing values is a complicated and
always awkward problem, when the data come from real-world contexts. In
applications, we are very often in front of observations for which all the
values are not available, and this can occur for many reasons: typing errors,
fields left unanswered in surveys, etc. Most of the statistical software (as
SAS for example) simply suppresses incomplete observations. It has no practical
consequence when the data are very numerous. But if the number of remaining
data is too small, it can remove all significance to the results. To avoid
suppressing data in that way, it is possible to replace a missing value with
the mean value of the corresponding variable, but this approximation can be
very bad when the variable has a large variance. So it is very worthwhile
seeing that the Kohonen algorithm (as well as the Forgy algorithm) perfectly
deals with data with missing values, without having to estimate them
beforehand. We are particularly interested in the Kohonen algorithm for its
visualization properties.
