Accurately extracting patterns that appear frequently only within specific time intervals, together with their dense intervals, is important in many applications such as understanding seasonal demand and detecting anomalous behavior.Frequent itemset mining evaluates support over the entire dataset and therefore cannot detect locally dense patterns. Existing methods for dense pattern mining with interval output estimate dense intervals through occurrence-gap constraints; however, since the gap constraint parameter governs both pattern identification accuracy and interval detection accuracy simultaneously, finding a parameter setting that achieves high accuracy for both objectives is difficult.In this paper, we propose Apriori-window, an exact algorithm that resolves this structural limitation. The proposed method directly evaluates local frequency within a sliding window and thus requires no gap constraint parameter, and it efficiently enumerates dense intervals through anti-monotonicity-based pruning of the search space and stride-skip reduction of the number of window scans. Experiments on three real-world datasets demonstrate that existing methods struggle to simultaneously achieve high accuracy in both pattern identification and dense interval detection, and scalability experiments on synthetic data confirm the practical applicability of the proposed method.

翻译：准确提取仅在特定时间区间内频繁出现的模式及其密集区间，在理解季节性需求和检测异常行为等众多应用中至关重要。频繁项集挖掘评估的是整个数据集上的支持度，因此无法检测局部密集模式。现有的具有区间输出的密集模式挖掘方法通过出现间隔约束来估计密集区间；然而，由于间隔约束参数同时控制模式识别准确性和区间检测准确性，因此很难找到一个参数设置来同时实现这两个目标的高准确性。在本文中，我们提出Apriori-window，一种解决此结构限制的精确算法。所提方法通过滑动窗口直接评估局部频率，因此无需间隔约束参数，并通过基于反单调性的搜索空间剪枝和跨度跳跃减少窗口扫描次数，高效地枚举密集区间。在三个真实世界数据集上的实验表明，现有方法难以同时在模式识别和密集区间检测方面实现高精度，而在合成数据上的可扩展性实验证实了所提方法的实际适用性。