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• ESTIMATING THE EFFECTIVENESS OF THE METHOD FOR SEARCHING THE ASSOCIATIVE RULES FOR THE TASKS OF PROCESSING BIG DATA

  V. V. Bova, E.V. Kuliev, S.N. Scheglov

  2020-07-20

  Abstract ▼

  The modern databases have significant volume and consist of large masses of information.
  One of the popular methods of knowledge identification in terms of tasks of analysis and processing
  of large data volumes is composed of the algorithms for searching the associative rules.
  The paper solves the problem of building the bases of associative rules for the analysis of the unstructured
  large data volumes on the basis of searching different regularities considering the importance
  of their characteristics. The authors propose the method for synthesizing the bases and
  building the transaction database to calculate the threshold values of support and application of
  criteria of estimating implicit associations. This allows us to extract repeated and implicit associative
  rules. To improve the computational effectiveness of extracting the associative rules, the paper
  applies the genetic algorithm for optimization of input parameters of the characteristic searching
  space. The developed method shortens the time of rules extraction, reduces the number of generated
  common rules, and avoid the resource-consuming procedure of pre-processing the synthesized
  rule base. The authors developed the program and algorithmic module to carry out the experimental
  research of the proposed method for synthesizing the associative rules on the basis of filtering
  the input parameters of the search model for solving the tasks of processing the unstructured
  data. The experiments conducted on the test transaction bases allow us to clarify the theoretical
  estimations of time complexity of the proposed method that used the genetic algorithm to calculate
  the weighed support of the set of rules considering the assessment of a priori informative content
  of the characteristics included in the dataset. The time complexity of the developed method is estimated
  as  О(I2). The comparative analysis is performed using the test data of the Retail Data
  with the algorithms Apriori and Frequent Pattern-Growth. The results have proven the effectiveness
  of the search method on big sets of transactions. The method allows us to reduce the cardinal
  of an irredundant set of extracted associative rules in more than 40% in comparison with the popular
  algorithms. The experiments have shown that the method can be effective for the tasks of
  knowledge discovery in terms of processing large volumes of data.