Mining Weighted Sequential Patterns Based on Customer Lifetime Value

Mining Weighted Sequential Patterns Based on Customer Lifetime Value

田燕山(Yan Shan Tien)

指導教授 ： 胡雅涵

國立中正大學/管理學院/資訊管理學系/碩士(2013年)

摘要

序列樣式探勘是資料探勘領域中一種很重要的應用。其中，它被廣泛應用在在購買行為分析的領域。透過序列樣式探勘，企業可以窺探顧客的購買習慣，藉此得知產品之間的關聯性與大多數顧客的購物喜好，進而制訂銷售方針。
然而，在過去的研究裡，所有顧客的序列都被視為重要性是相等的，但現實中每一個顧客對企業組織而言，一般來說其重要性不盡相同。因此，加權序列樣式探勘的議題開始誕生。然而，在購買行為上，顧客終身價值是衡量顧客重要程度很好的分法，到目前為止還沒有以顧客價值的角度來做加權序列樣式探勘。本研究以各種顧客終身價值模式來計算序列的權重，並調整Prefixspan 演算法做序列樣式探勘。在實驗中測試執行時間、樣式數量、樣式平均價值、查準率、查全率、F測量等指標來比較我們的方法與傳統方法的差異。

關鍵字

RFM模式 ； Prefixspan演算法 ； 加權序列樣式探勘 ； 序列樣式探勘 ； 顧客終身價值

並列摘要

Sequential pattern mining (SPM) is one of most important data mining technique, and it is widely used in customer behavior scenario. Organizations are able to explore customers’ purchase habit and comprehend the relationship between merchandise and each other through SPM process to develop sales policy.
However, all customers’ sequence is regarded as with the same importance in the past research. But in reality, each customer generally has a different significance to organization in most of case. Therefore, weighted sequential pattern mining (WSPM) has been coming up to discuss. Customer lifetime value (CLV) is a remarkable way to distinguish between customers in terms of their importance. Nevertheless, there is no research about using CLV to do WSPM until now. This research utilize different CLV model to calculate weights of sequences and adjust Prefixspan algorithm to do WSPM. In experiment, we test runtime, number of patterns, value per pattern, precision, recall, F-measure to compare performance between our method and traditional SPM.

並列關鍵字

Customer Lifetime Value ； Weighted Sequential Pattern Mining ； Sequential Pattern Mining ； RFM Model ； Prefixspan Algorithm

參考文獻

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