Adaptive buffer strategy for regular High-Utility sequential pattern Mining on incremental data
Abstract
Regular High-Utility Sequential Pattern Mining (RHUSP) from incremental databases poses a significant challenge in balancing computational efficiency and accuracy. Current algorithms using a fixed buffer ratio (µ) often suffer from limitations: a low µ value results in wasted memory resources, while a high µ carries the risk of missing important patterns. This paper proposes Adaptive-RIncHusp, an algorithm featuring an adaptive buffer strategy that automatically adjusts the parameter µ for each data batch update. The method integrates three complementary heuristics: utility Ratio, Threshold difficulty, and growth rate, combined with a high-sensitivity risk function (f x)=x0.25) for early detection of data fluctuations. Experiments on 5 benchmark datasets demonstrate that Adaptive-RIncHusp is 8-15% faster and saves 20-40% memory compared to the conservative fixed strategy (µ=0.4) while maintaining recall > 97%. Furthermore, the algorithm achieves speeds comparable to the aggressive fixed strategy (µ=0.9) while completely eliminating pattern loss, confirming its effectiveness in volatile data environments.
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