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    Area of Science:

    • Computer Science
    • Data Mining
    • Database Systems

    Background:

    • Exponential data growth and high-dimensional data hinder knowledge discovery.
    • Existing association rule mining algorithms face challenges with time and memory for large datasets.

    Purpose of the Study:

    • Introduce a new data structure to enhance existing association rule mining algorithms.
    • Improve the efficiency and performance of data mining processes without altering original schemas.

    Main Methods:

    • Developed a data structure employing a Hamming distance-based shuffling strategy for data sorting.
    • Integrated an inverted index mapping and run-length encoding compression.
    • Tested the structure with diverse datasets containing millions of items and records.

    Main Results:

    • Demonstrated orders-of-magnitude improvement in algorithm runtime.
    • Achieved substantial reductions in auxiliary and main memory requirements.
    • Validated the data structure's utility across various large-scale datasets.

    Conclusions:

    • The proposed data structure effectively accelerates association rule mining.
    • It offers significant memory and performance benefits for handling large, high-dimensional data.
    • This approach enhances the efficiency of knowledge discovery from big data.