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 本研究之目的在於探討有效率的維護資訊性關聯規則（Informative Rule Sets, IRS）的方法。以信心度而言，資訊性關聯規則可以和一般的關聯規則做相同的預測，且規則的數量會遠小於關聯規則的數量。預測是指給定一群顧客在某段時間內購物行為之規則以及某一特定顧客之部分購物行為，希望能預測出此一特定顧客之其他購物行為。而資訊性關聯規則之維護是指已知交易資料庫及其資訊性關聯規則，當資料庫發生新增、刪除、或修改時，如何有效率的維護資訊性關聯規則。 根據關聯規則之快速更新（Fast Update, FUP）的方法，本研究提出兩個有效率的維護資訊性關聯規則的演算法。當資料庫發生新增或刪除資料時，漸近新增演算法可有效率的維護資訊性關聯規則。當資料庫發生刪除資料時，漸近刪除演算法可有效率的維護資訊性關聯規則。同時我們並與非漸近演算法作數值比較，結果顯示我們所提出之方法需要較少之資料庫掃瞄次數、候選規則、及執行時間。
 The goal of this research is to study the efficient maintenance of discovered Informative Rule Set (IRS) when new transaction data is added to and/or deleted from original transaction database. An informative rule set is the smallest subset of association rule set such that it can make the same prediction sequence according to confidence priority. Prediction is a process, for example, given a set of rules that describe the shopping behavior of the customers in a store over time, and some purchases made by a particular customer, we wish to predict what other purchases will be made by that customer. The problem of maintenance of discovered informative rule set is that, given a transaction database and its informative rule set, when the database receives insertion, deletion, or modification, we wish to maintain the discovered informative rule set as efficiently as possible. Based on the Fast Updating technique (FUP) for the updating of discovered association rules, we present here two algorithms to maintain the discovered IRS. The proposed incremental insertion algorithm maintains the discovered IRS efficiently under database insertion. The proposed incremental deletion algorithm maintains the discovered IRS efficiently under database deletion. Numerical comparison with the non-incremental informative rule set approach is shown to demonstrate that our proposed techniques require less computation time, in terms of number of database scanning and number of candidate rules generated, to maintain the discovered informative rule set.
 ABSTRACT (CHINESE) III ABSTRACT (ENGLISH) V ACKNOWLEDGEMENTS VII LIST OF FIGURES IX LIST OF TABLES X CHPATER 1 INTRODUCTION 1 1.1 Background 1 1.2 Motivation 2 1.3 Thesis Organization 3 CHAPTER 2 LITERATURE SURVEY 4 2.1 Association Rules for Prediction 4 2.2 Informative Rule set for Prediction 8 2.3 Maintenance of Association Rules 10 CHAPTER 3 DISCOVERY OF INFORMATIVE RULE SET: INCREMENTALINSERTION 14 3.1 Problem Description 14 3.2 Notations 15 3.3 Algorithm 16 3.4 Example 21 CHAPTER 4 DISCOVERY OF INFORMATIVE RULE SET: INCREMENTALINSERTION 23 4.1 Problem Description 24 4.2 Algorithm 25 4.3 Example 29 CHAPTER 5 EXPERIMENT RESULTS 33 5.1 Incremental Insertion Results.………………………………………………33 5.2 Incremental Deletion Results.………………………………………………37 CHAPTER 6 CONCLUSION 41 REFERENCE 43
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