The apriori method in the collection of significant values of pharmaceutical data

Abstract

The paper demonstrates the relevance and necessity of seeking efficient algorithms for large-scale data applications. A review of existing approaches and methods for analyzing unstructured data, often found in pharmaceutical data and medical research results, has been conducted, providing a description of the characteristics of such data. It is indicated that Data Mining methods, including the Apriori algorithm, are applicable for discovering hidden patterns in large volumes of data. Modern implementation tools of the Apriori algorithm for parallel and distributed processing on platforms such as MPI, OpenMP, Hadoop, OpenCL, Spark, and Apache Flink are discussed. The specifics of implementing the Apriori algorithm on each platform are described. Theoretical recommendations are provided, and the results of applying the platforms in terms of the complexity (required workload) and time to obtain results are forecasted. A computational experiment is conducted, which overall confirms the theoretical conclusions presented in the concluding sections of the paper and outlines avenues for further research, envisaging deeper experimentation with further application of parallel and distributed versions of association search algorithms in various fields, including medical and pharmacological domains.