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博碩士論文 etd-0713113-165134 詳細資訊
Title page for etd-0713113-165134
論文名稱
Title
多點架位資料探勘之研究
A Study of Multi-site On-shelf Data Mining
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
97
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2013-07-10
繳交日期
Date of Submission
2013-09-02
關鍵字
Keywords
資料挖掘、關聯規則挖掘、時序關聯規則、架位期間、多點環境
data mining, temporal association rule, association-rule mining, on-shelf time period, multi-site environment
統計
Statistics
本論文已被瀏覽 5758 次,被下載 152
The thesis/dissertation has been browsed 5758 times, has been downloaded 152 times.
中文摘要
時序資料挖掘技術被廣泛的用於從資料庫中,擷取出有意義的時間相關樣式。然而,目前大部份既存相關研究僅是考慮不同項目展售期去找出時序關聯規則。在實際應用裡,某些商品是會多次在商店中上架或下架進行銷售的,所以若未考慮項目上架展售期,則便會遺失某些關聯規則。另一方面,一家大型企業擁有多個在不同地點的商店或分公司是常見的。若能彈性地在多商店環境裡獲得具有上架地點與時間考量之關聯規則,發展一有效架構來提供線上決策資訊給使用者則是一個重要的議題。在本論文中,我們處理了具有商品架位概念之時序關聯規則與多點環境下具有商品架位概念關聯規則等問題。
在論文的第一部分,對於具有商品架位期間因素考慮之時序關聯規則,我們提出一個稱為架位關聯規則挖掘之新研究議題。然而,由於此探勘問題不具有向下封閉此特性,所以是更難於傳統關聯規則挖掘問題。為了能有效處理此問題,我們提出了一個有效的三階段挖掘演算法。在論文的第二部分,對於商品多點上架問題,我們也提出一個延伸自架位關聯規則挖掘之線上多點架位關聯規則挖掘新議題,並也提一個有效線上挖掘方法來處理多點架位關聯規則之挖掘問題。此外,為了更進一步節省計算時間與I/O時間等成本,一個以多點架位關聯規則資訊為基礎的策略亦被設計來緊縮項目集之支持度邊際值,以便能提前刪除大量無效的多點架位候選項目集。在實驗中,本論文將評估兩種所提出的規則之有效性,同時亦探討在不同的參數設定下,所提出的兩個挖掘方法之執行效率。
Abstract
Temporal data mining techniques have been widely used to extract desirable time-related knowledge from existing databases. However, most of the existing studies only considered different lifespans of items in a set of transactions to find general temporal association rules. In reality, some products in a store may be put on shelf and taken off shelf multiple times, and some biases may exist in the temporal association rules discovered. Besides, it is common for a company to have a chain of retail sites in different locations, and it is thus very critical to flexibly obtain association rules from portions of data with the consideration of on-shelf situations of items in a multi-site environment for providing relevant online decision supports to users. In this thesis, we thus handle the problems of mining temporal association rules with the consideration of on-shelf situations of items in a multi-site environment.
In the first part of the thesis, we introduce a new research issue named on-shelf association rule mining (OAR) for temporal association rule mining with the consideration of time periods of items. This problem is more difficult than traditional association-rule mining due to this issue without the downward-closure property. Hence, an effective three-phase mining approach is developed to find such rules in a temporal database. In the second part of the thesis, we introduce another new issue named online multi-site on-shelf association rule mining (MOAR) with the consideration of on-shelf locations and time periods of items. Meanwhile, an online mining approach is also developed for generation of online multi-site on-shelf association rules, and an effective strategy is designed to tighten the upper-bounds of supports for candidate itemsets by using the multi-site pattern relation information.
The experimental results on simulation datasets show the effectiveness of the two types of rules (OAR and MOAR) and the performance of the proposed mining approaches under different parameter settings.
目次 Table of Contents
論文審定書 i
致謝 ii
摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables viii
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Contributions 3
1.3 Organization of Thesis 4
CHAPTER 2 Related Work 6
2.1 Association-Rule Mining 6
2.2 Temporal Data Mining 8
2.3 Online Data Mining 10
CHAPTER 3 Considering On-shelf Time Periods of Items in Mining Temporal Association Rules 15
3.1 Introduction 15
3.2 Problem Statement and Definitions 17
3.3 The Proposed Algorithm, TP-OAR 21
3.4 An Example of Using TP-OAR 26
3.5 Experimental Results 37
3.5.1 Experimental Datasets 38
3.5.2 Evaluation on Effectiveness of On-shelf Itemsets 39
3.5.3 Efficiency Evaluation 41
CHAPTER 4 Online Multi-site On-shelf Association Rule Mining 43
4.1 Introduction 43
4.2 Problem Statement and Definitions 45
4.3 Proposed Mining Algorithm, FP-OARq 57
4.4 An Example of Using FP-OARq 61
4.5 Experimental Results 71
4.5.1 Experimental Datasets 71
4.5.2 Evaluation on Effectiveness of On-shelf Itemsets 72
4.5.3 Efficiency Evaluation 75
CHAPTER 5 Conclusion and Future Work 77
Reference 79
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