因此，為了提升週期分析的準確度與演算法的適用性，本論文修改原先PIM Algorithm對資料所作之預處理，針對產品重複購買行為趨勢重新定義，改良 PIM Algorithm 的缺點，提出一以『Divide and Conquer』為核心之完整週期分析演算法 Modified Periodical Intervals Mining Algorithm（MPIM Algorithm），分析消費者重複購買行為的週期，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。 Periodical Intervals Mining Algorithm (PIM Algorithm) is an algorithm for analyzing the periodical properties of time intervals over sequential pattern mining. However, PIM Algorithm does not make a more detailed discussion on the purchase behavior, it will affect the accurate rate of periodicity detection. Moreover, PIM Algorithm is only suited for the pure ascending or descending type distribution function. In a real-world scenario, purchasing behavior is extremely dynamic, it is only taken minority to fit the type distribution function mentioned above.
As a result, the aim of this work is to improve accuracy of the periodicity analysis and applicability of PIM algorithm. The study revises the preprocess of data, redefines the trend of the repeat buying behavior, and improves the shortcoming of PIM Algorithm. The Modified Intervals Mining Algorithm (MPIM Algorithm) takes the divide-and-conquer approach to collect the knowledge of the designated distribution function. A good agreement has been found between the analytical and experimental result shows good agreement.
