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A Novel Approach to Discover Fuzzy Frequent Georeferenced Patterns in Massive Georeferenced Temporal Quantitative Databases
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DOI：10.1109/tfuzz.2026.3701309.png)
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Abstract
En 中文
Fuzzy frequent pattern mining is a popular data science technique for identifying fuzzy frequent patterns in quantitative transactional databases. Most existing studies have focused mainly on the occurrence frequency and have overlooked the spatial (or georeferenced) and temporal characteristics commonly present in real-world datasets. To address this limitation, this article proposes a novel model for discovering fuzzy frequent georeferenced patterns (FFGPs) in quantitative geo-referenced temporal databases (QGTDs). Mining FFGPs is challenging due to the vast search space. To overcome this challenge, we introduce a technique called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neighborhood pruning</i>, which effectively reduces the search space and computational cost. This technique makes the mining process more scalable and suitable for practical applications. In addition, an efficient algorithm, FFGP-Miner, is developed to identify all significant patterns within QGTDs. Experimental evaluations on multiple datasets demonstrate that FFGP-Miner achieves superior performance in terms of memory efficiency and processing time. Finally, a case study on air pollution data illustrates the practical utility of the proposed model in uncovering meaningful environmental insights.
Keywords:
Air pollution analysis
fuzzy frequent pattern mining
neighborhood pruning
spatiotemporal databases
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