Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data

This paper introduces a dual-strategy model based on temporal transformation and fuzzy theory, and designs a partitioned mining algorithm for periodic frequent patterns in large-scale event data (3P-TFT). The model reconstructs original event data through temporal reorganization and attribute fuzzification, preserving data continuity distribution characteristics while enabling efficient processing of multidimensional attributes within a multi-temporal granularity calendar framework. The 3P-TFT algorithm employs temporal interval and object attribute partitioning strategies to achieve distributed mining of large-scale data. Experimental results demonstrate that this method effectively reveals hidden periodic patterns in stock trading events at specific temporal granularities, with volume–price association rules providing significant predictive and decision-making value. Furthermore, comparative algorithm experiments confirm that the 3P-TFT algorithm exhibits exceptional stability and adaptability across event databases with various cycle lengths, offering a novel theoretical tool for complex event data mining.