  The analysis of enormous datasets with missing data entries is a standard
task in biological and medical data processing. Large-scale, multi-institution
clinical studies are the typical examples of such datasets. These sets make
possible the search for multi-parametric relations since from the plenty of the
data one is likely to find a satisfying number of subjects with the required
parameter ensembles. Specifically, finding combinatorial biomarkers for some
given condition also needs a very large dataset to analyze. For this goal,
statistical regression analysis is not the preferred tool of choice, since (i)
the {\em a priori} knowledge of the parameter-sets to analyze is missing, and
(ii) typically relatively few subjects have the interesting parameter-value
ensembles for the analysis. For fast and automatic multi-parametric relation
discovery association-rule finding tools are used for more than two decades in
the data-mining community. Here we present the SCARF webserver for {\em
generalized} association rule mining. Association rules are of the form: $a$
AND $b$ AND ... AND $x \rightarrow y$, meaning that the presence of properties
$a$ AND $b$ AND ... AND $x$ implies property $y$; our algorithm finds
generalized association rules, since it also finds logical disjunctions (i.e.,
ORs) at the left-hand side, allowing the discovery of more complex rules in a
more compressed form in the database. This feature also helps reducing the
typically very large result-tables of such studies, since allowing ORs in the
left-hand side of a single rule could include dozens of classical rules. The
capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer's
database of the Coalition Against Major Diseases (CAMD) in our recent
publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300-307,
2017). Here we describe the webserver implementation of the algorithm.
