Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data.
The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data.
Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied.
[citation needed] are highly accurate with good and representative samples of the problem, but perform badly with biased data.
It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces.