臺灣博碩士論文加值系統

現今的商業環境下，大部分的中大型企業都有其分支營運據點（分公司、子公司、連鎖商店或是分店等等），這些分支營運據點有可能遍佈整個國家甚至是全球。然而，傳統的頻繁樣式探勘方法並不適用於現今多商店的環境，因此，在過去幾十年來，許多研究便針對多商店的環境來設計進階版頻繁樣式探勘演算法。
然而，在這些進階版的頻繁樣式探勘演算法當中，我們發現或有考量多商店環境，但忽略在雲端運算環境中去探究的議題，例如：雲端運算成本與安全性；有些則是有考量雲端運算環境下的頻繁樣式探勘但忽略多商店環境中所需考量的時空背景差異對於探勘結果的影響。
因此，本研究將提出新型的頻繁樣式探勘演算法，主要是因應需求且考量雲端運算環境限制而設計的頻繁樣式探勘演算法- A Request-Based Frequent Pattern Mining Algorithm(FFPM) in Multi-Store Environment，希望能在同時考量雲端運算成本以及資料安全性的條件之下可快速且彈性的依據用戶需求探勘出不同情境組合下之頻繁樣式項目集。
本研究之實驗分為四個部分來進行，主要目的為評估FFPM的整體執行時間效率，進而與傳統頻繁樣式探勘演算法做執行時間效率上的比較，最後將兩種演算法分別探勘模擬資料及真實資料，再將其結果做比對。實驗結果顯示，不論在時間效率亦或是探勘結果上，相對於傳統方法而言FFPM皆能有良好的表現，能更有效率地挖掘出不同情境組合下的頻繁樣式。

In today’s business environment, most of the companies have branches, subsidiaries, chain stores or dealers. And these branches could be located in many different geographical locations. However, the traditional frequent pattern mining algorithm might not be suitable in the multi-store environment nowadays. Therefore, over the last few decades, there have been many studies focus on advanced frequent pattern mining algorithm in a multi-store environment.
However, we found that some of the existing methods consider only the multi-store environment problem but might not think about the cloud computing issues, for example, the cost and privacy issues in the cloud computing environment. In contrast, some of them overcome the cloud computing environment issue but not focus on frequent pattern mining in a multi-store environment.
In this paper, we propose a new algorithm to remedy this research gap – A Request-Based Frequent Pattern Mining Algorithm (FFPM). We focus on the request-based algorithm and also expect that those frequent patterns under different stores and time periods are considered while considering those cost limitation and privacy problem in the cloud computing environment.
To evaluate the efficiency and effectiveness of the proposed method, four experiments are performing in this study. The first experiment is about the running time of FFPM, and the second and third experiments are efficiency and effectiveness of FFPM compared with Apriori algorithm. The last experiment is evaluating FFPM by using a real data set, called SC-POS. Results of this study show that not only on the running time or precision but the proposed method can also find out those frequent patterns under different stores and time periods even more efficiently compared with the traditional frequent pattern mining algorithm.

謝誌 ........................................................................................................................................... i
摘要 .......................................................................................................................................... ii
Abstract .................................................................................................................................. iii
目錄 ......................................................................................................................................... iv
表目錄 ...................................................................................................................................... v
圖目錄 ..................................................................................................................................... vi
第一章 緒論 .......................................................................................................................... 1
第二章 文獻探討 ................................................................................................................. 11
第一節 APRIORI-LIKE 頻繁樣式探勘 .................................................................................... 12
第二節 NON-APRIORI-LIKE 頻繁樣式探勘 ........................................................................... 12
第三節 基於雲端頻繁樣式探勘方法 ...................................................................................... 16
第四節 考量時空組合情境之頻繁樣式探勘方法...................................................................... 18
第三章 研究方法 ................................................................................................................. 20
第一節 問題定義 ................................................................................................................ 20
第二節 研究方法 ................................................................................................................. 32
第四章 實驗設計與結果分析 ................................................................................................ 42
第一節 模擬實驗一 .............................................................................................................. 45
第二節 模擬實驗二 .............................................................................................................. 50
第三節 模擬實驗三 .............................................................................................................. 52
第四節 真實資料實驗 (SC-POS DATASET) .......................................................................... 58
第五章 結論 ........................................................................................................................ 63
參考文獻 ................................................................................................................................. 65

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