This invention discloses a method for accurate frequent itemset mining of
streaming data, relating to the field of
data mining technology, including the following steps: S1, accessing and managing structured
streaming data through a distributed streaming
processing engine; S2, converting streaming
transaction data into a vertical format of item and transaction ID sets, and maintaining the vertical
database state using
state management functions; S3, filtering infrequent items based on a minimum support threshold, performing equivalence class partitioning, and partitioning the conditional
database to each computing node based on the equivalence classes; This invention, by deeply integrating an incremental mining architecture with a vertical
data format with a distributed streaming
processing engine, ensures the complete accuracy of frequent itemset mining results while achieving a high
throughput of tens of thousands of transactions per second, thus successfully breaking through the technical
bottleneck of "difficulty in balancing accuracy and efficiency" in existing technologies, and achieving significant technical progress.