The order-preserving pattern mining can be regarded as discovering frequent trends in time series, since the same order-preserving pattern has the same relative order which can represent a trend. However, in the case where data noise is present, the relative orders of many meaningful patterns are usually similar rather than the same. To mine similar relative orders in time series, this paper addresses an approximate order-preserving pattern (AOP) mining method based on (delta-gamma) distance to effectively measure the similarity, and proposes an algorithm called AOP-Miner to mine AOPs according to global and local approximation parameters. AOP-Miner adopts a pattern fusion strategy to generate candidate patterns generation and employs the screening strategy to calculate the supports of candidate patterns. Experimental results validate that AOP-Miner outperforms other competitive methods and can find more similar trends in time series.

翻译：保序模式挖掘可视为发现时间序列中频繁趋势的方法，因为同一保序模式具有相同的相对顺序，能够表示一种趋势。然而，在存在数据噪声的情况下，许多有意义的模式的相对顺序通常相似而非完全相同。为挖掘时间序列中相似的相对顺序，本文提出了一种基于(δ-γ)距离的近似保序模式（AOP）挖掘方法，以有效度量相似性，并设计了名为AOP-Miner的算法，根据全局与局部近似参数挖掘AOP。AOP-Miner采用模式融合策略生成候选模式，并利用筛选策略计算候选模式的支持度。实验结果表明，AOP-Miner优于其他竞争方法，能够发现时间序列中更多相似趋势。