一個有效的使用多重門檻值挖掘關聯性規則演算法

Abstract

近年來，隨著電子商務的蓬勃發展，掌握顧客的消費習慣變成提高業積的絕佳方法。因此，使用資料挖掘中找尋關聯規則的方法快速找出顧客的購買規則也變得越來越重要，而搜尋頻繁物件集是整個過程中很重要的一個程序。在本篇論文中，我們首先提出一個利用 Partition 演算法的觀念及 Early Pruning 技巧來更快找到頻繁物件集的 Early Pruning Partition 演算法(簡稱 EPP)。接下來，我們在 EPP 演算法中整合多門檻值的檢查，來建構我們的 Multiple Thresholds Early Pruning 演算法。我們的 MTEPP 演算法可以在多個最小支持度的條件設定下，更快地找出對應某些購買行為的頻繁物件集。在本文的最後，我們針對 EPP 及 MTEPP 演算法進行數項實驗，更進一步驗證其優越的執行效能以及的確能找出別的演算法找不出的頻繁物件集。我們會在接下來的本文中介紹這兩個演算法的詳細內容。

Catering the buying behaviors of customers becomes more and more important by the popularization of E-Commerce recently. How to find the association rules efficiently from the transaction records is one of the most interesting topics to be investigated. In this thesis, at, first, we propose en efficient algorithm, named Early Pruning Partition algorithm (EPP), with extending the concept of Partition algorithm and using an early pruning technology to improve the performance of mining frequent itemsets under single minimum support. Then we add the checking of multiple thresholds in EPP algorithm to construct our Multiple Thresholds Early Pruning Partition algorithm (MTEPP). Our MTEPP algorithm can find more effective frequent itemsets corresponding to some events of buying behavior. For evaluating our algorithm, we also implement a simulation environment to verify it. According to our evaluations, our algorithms perform a well performance and find the more useful frequent itemsets indeed. The detailed descriptions of our algorithms will be given in the contents.