• Nataliya Shakhovska
  • Roman Kaminskyy
  • Eugen Zasoba
  • Mykola Tsiutsiura



Big data, association rule, data dependency, Apriori, Complexity, parallel processing.


The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.


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How to Cite

Shakhovska, N., Kaminskyy, R., Zasoba, E., & Tsiutsiura, M. (2018). ASSOCIATION RULES MINING IN BIG DATA. International Journal of Computing, 17(1), 25-32.