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Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases

Finding fuzzy frequent patterns in a quantitative database is a challenging problem of significant importance in many real-world applications. Past studies focused on mining these patterns in quantitative transactional databases by disregarding the spatiotemporal characteristics of an item in the database. This paper proposes a generic model of fuzzy frequent spatial pattern (FFSP) that may exist in a quantitative spatiotemporal database. Discovering FFSPs in a database is nontrivial and challenging due to its huge search space and high computational cost. A novel pruning technique, called neighborhood pruning, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FFSPs in large real-world databases practicable. We also present an efficient algorithm, called Fuzzy Frequent Spatial Pattern-Miner (FFSP-Miner), to find all desired patterns in the database. Experimental results demonstrate that FFSP-Miner is both memory and runtime efficient. Finally, we discuss the usefulness of our model with a case study on air pollution analytics.