Selected Data Mining Tools for Data Analysis in Distributed Environment

In this paper, we deal with distributed data represented either as a finite set [Formula: see text] of decision tables with equal sets of attributes or a finite set [Formula: see text] of information systems with equal sets of attributes. In the former case, we discuss a way to the study decision trees common to all tables from the set [Formula: see text]: building a decision table in which the set of decision trees coincides with the set of decision trees common to all tables from [Formula: see text]. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply various decision tree learning algorithms to it. We extend the considered approach to the study of test (reducts) and decision rules common to all tables from [Formula: see text]. In the latter case, we discuss a way to study the association rules common to all information systems from the set [Formula: see text]: building a joint information system for which the set of true association rules that are realizable for a given row [Formula: see text] and have a given attribute a on the right-hand side coincides with the set of association rules that are true for all information systems from [Formula: see text] , have the attribute a on the right-hand side, and are realizable for the row [Formula: see text]. We then show how to build a joint information system in a polynomial time. When we build such an information system, we can apply various association rule learning algorithms to it.