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| 研究生： |
江美靜 Mei-Ching Chiang |
|---|---|
| 論文名稱： |
有時間區間的循序挖掘 |
| 指導教授： |
陳彥良
Yen-Liang Chen |
| 口試委員: | |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 資訊管理學系 Department of Information Management |
| 畢業學年度： | 90 |
| 語文別： | 中文 |
| 論文頁數： | 34 |
| 中文關鍵詞： | 資料挖掘 、循序樣式 |
| 外文關鍵詞： | data mining, sequential pattern |
| 相關次數： | 點閱：11 下載：0 |
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以往一般的循序挖掘研究中所探討的循序樣式，只能得知樣式中項目之間的前後順序關係，但無法讓我們知道項目之間的發生是間隔多久的時間，例如”有70%的機會，顧客於某商店購買印表機後，他會再來購買掃描器，之後則會再來購買燒錄器”的循序樣式，我們能從中得到印表機、掃描器、燒錄器的購買順序之資訊，但是我們無法得知購買印表機、掃描器、燒錄器之間的間隔時間為多久，因此本文提出包含時間區間的循序挖掘之研究，以探勘出擁有更多資訊的時間區間循序樣式，例如”有70%的機會，顧客於某商店購買印表機後，在經過6個月後，他會再來購買掃描器，之後，再經過3個月，則會再來購買燒錄器”的時間區間循序樣式。以零售業為例，業主可以利用所挖掘出的時間區間循序樣式，以瞭解顧客的習慣、喜好和需求，並且預測出顧客在未來某段時間內的期望，達到在適當的時機內，向適當的顧客，提供適當的產品和服務，滿足顧客的所需與所求之目的。故時間區間循序樣式可為企業帶來競爭優勢或替個人帶來利益。
本研究的目的是要挖掘出在序列資料庫中的時間區間循序樣式，一方面我們對此時間區間循序挖掘問題作出相關的定義，另一方面，我們發展出兩種演算法 — I-Apriori和I-PrefixSpan以進行挖掘。最後的實驗分析中，我們將演算法實作成系統，以驗證方法的可行性，並測試此兩演算法的效能與scale-up的特性。從實驗結果中得知，I-PrefixSpan的效能和scale-up的能力皆勝過I-Apriori，為一個較佳的時間區間循序挖掘的演算法。
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