  In the recent decade companies started collecting of large amount of data.
Without a proper analyse, the data are usually useless. The field of analysing
the data is called data mining. Unfortunately, the amount of data is quite
large: the data do not fit into main memory and the processing time can become
quite huge. Therefore, we need parallel data mining algorithms. One of the
popular and important data mining algorithm is the algorithm for generation of
so called frequent itemsets. The problem of mining of frequent itemsets can be
explained on the following example: customers goes in a store put into theirs
baskets some goods; the owner of the store collects the baskets and wants to
know the set of goods that are bought together in at least p% of the baskets.
Currently, the sequential algorithms for mining of frequent itemsets are quite
good in the means of performance. However, the parallel algorithms for mining
of frequent itemsets still do not achieve good speedup. In this thesis, we
develop a parallel method for mining of frequent itemsets that can be used for
an arbitrary depth first search sequential algorithms on a distributed memory
parallel computer. Our method achieves speedup of ~ 6 on 10 processors. The
method is based on an approximate estimation of processor load from a database
sample - however it always computes the set of frequent itemsets from the whole
database. In this thesis, we show a theory underlying our method and show the
performance of the estimation process.
