序列型樣之週期性與趨勢分析

Tendency and Periodicity of Repeated Buying Patterns

朱奐禎(Huan-Chen Chu)

指導教授 ： 蔣定安

淡江大學/工學院/資訊工程學系碩士班/碩士(2009年)

https://doi.org/10.6846/TKU.2009.00302

摘要

Periodical Intervals Mining Algorithm （PIM Algorithm）為一針對序列型樣於時間分佈用來挖掘潛在週期的演算法，藉由 PIM Algorithm 可針對時間間隔將週期分佈挖掘出來並分析出序列型樣的間隔天數。然而PIM Algorithm 並無針對單一消費行為趨勢作更詳細之探討，使得該週期分析所得結果之準確度會有較大誤差。此外，PIM Algorithm 僅能分析單純具有完全遞增/遞減型的購買週期分佈趨勢，然而在現實生活中，購買行為週期的趨勢分佈會呈現很大的變動性，具有完全遞增/遞減型的購買週期分佈只佔少數。

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。

關鍵字

序列型樣 ； 時間間隔 ； 週期分佈 ； 迴歸分析

並列摘要

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.

並列關鍵字

Sequential Patterns ； Time Interval ； Periodical Distribution ； Polynomial Regression

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