本篇論文提出一套用來分析序列型樣對於時間間隔是否潛在週期之演算法，首先我們提出PDT/PDM演算法用來尋找曲線的週期性分布，並將之延伸在遞增/遞減分佈的曲線上修正為LPDT/LPDM演算法，最後我們根據序列型樣對於時間間隔的曲線分布特徵將上述方法歸納為PIM (Periodical Intervals Mining Algorithm)演算法，並將時間間隔的週期起伏挖掘出來，藉由所有產品之間序列的銷售週期比較出最佳推薦產品的銷售時間點以提供產品行銷的最有利資訊。 In processing huge transaction data analysis, we often use Association Rules Mining and Sequential Patterns Mining techniques to discover the buying behaviors of customers. However, by sequential patterns, we are hard to find out the time intervals of related items purchased.
In this paper, we develop a set of algorithms to analysis the periodical properties of time intervals over sequential patterns. The first, we introduce PDT/PDM algorithms to discover periodical distributions for common cases. Then, we extend them as LPDT/LPDM algorithms to overcome linearly trend components of curves. Finally, we combine those algorithms and sequential patterns’ distribution property as PIM (Periodical Intervals Mining) algorithm. By experiment, we use PIM algorithm to analysis the periodical distributions and use them to point out the best choice of products from sequential patterns by compare the periodical intervals.