Dynamic Support-Driven Adaptive Frequent Pattern Mining for Incremental Databases

This paper presents an adaptive incremental FP-Growth framework for mining frequent patterns in evolving transactional databases. The proposed method dynamically regulates the minimum support threshold and incrementally maintains frequent patterns without rebuilding the mining structure, resulting in improved processing efficiency and reduced memory consumption.