Common Association Rules for Dispersed Information Systems

Mikhail Moshkov, Beata Zielosko, Evans Teiko Tetteh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Association rules are popular form for knowledge discovery domain. They are used for finding interesting relationships and patterns hidden in large data sets and in the area of associative classification, where usually rules with one item in the right-hand side are considered. There are many different approaches and algorithms for mining association rules. One of the most popular group are methods which are based on mining frequent itemsets, usually applied for data in transaction format. Such data can be transformed to binary information system which corresponds to matrix data format. Technological development means that we are dealing with an increasing amount of data that can be heterogeneous, taking into account their format and location. In this paper, we assume that dispersed data is represented by a finite set S of information systems with equal sets of attributes. We discuss one of the possible ways to the study association rules common to all information systems from the set S: building a joint information system for which the set of true association rules that are realizable for a given row r and have given attribute f on the right-hand side coincides with the set of association rules that are true for all information systems from S, are realizable for the row r, and have the attribute f on the right-hand side. We show how to build a joint information system in a polynomial time. When we build such an information system, we can apply to it various association rule learning algorithms.
Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Pages4613-4620
Number of pages8
DOIs
StatePublished - Oct 19 2022

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