Open AccessASSOCIATION RULES MINING IN BIG DATA
Author(s) -
Nataliya Shakhovska,
Roman Kaminskyy,
Eugen Zasoba,
Mykola Tsiutsiura
Publication year - 2018
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.17.1.946
Subject(s) - association rule learning , computer science , data mining , big data , task (project management) , association (psychology) , process (computing) , set (abstract data type) , class (philosophy) , domain (mathematical analysis) , artificial intelligence , mathematics , mathematical analysis , philosophy , management , epistemology , economics , programming language , operating system
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.