臺灣博碩士論文加值系統

挖掘權重關聯規則的特性主要是考慮商品在交易中的重要性，使得與重要商品相關的關聯規則能被挖掘出來；而挖掘量化關聯規則的主要目的，則是從交易資料庫中找出大部分的客戶購買了哪些數量的商品，也會同時購買哪些數量的其他商品。然而，權重關聯規則沒有考慮商品被購買的數量，而量化關聯規則也沒有考慮到商品本身的利潤或重要性。在經濟學中提到影響成本的最終皆與數量有關；而高單價的商品也未必是獲利最高的產品，由此可知只考慮商品本身的利潤或是只考慮商品被購買的數量必定有不足的地方。因此同時考慮商品本身的利潤與其被購買的數量。本論文以計算商品其真正所代表的利潤價值，並提出資料探勘的方法找出商品與其被購買的數量範圍有達到使用者所指定利潤門檻值的商品組合，進而產生高效益的量化關聯規則。本論文所提出的方法不需產生候選商品組合，而且只需掃描原始交易資料庫一次，產生與某些商品組合相關的子資料庫，再針對這些子資料庫，就可以找出與這些商品組合相關的高效益量化關聯規則。

Mining weighted association rules consider the importance of items in a large transaction database. Mining quantitative association rules find most quantitative itemsets , which are purchased frequently, and relate with them from a large transaction database. However, weighted association rules didn’t consider the items which their quantities, and quantitative association rules didn’t consider the items which their weighted. Economics mention influence that quantities affect the cost; and high prices are not necessarily to make a profit, that proves, if only consider weighted or quantitative, it’s must not enough. This paper will consider both weighted and quantitative, and find out useful rules for policymaker. We will weight of items multiply quantitative of items, it’s mean utility, and we want to find high utility association rules that these items reach to the utility threshold. Our methods don’t produce candidates and just scan once database to produce about sub-database, then we use these sub-database to find profitable association rules.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 viii
1.緒論 1
2.相關研究 5
2.1關聯規則演算法 5
2.2加權關聯規則演算法 7
2.3量化關聯規則演算法 10
2.4高效益的關聯規則演算法 13
3.高效益的量化關聯規則探勘演算法 17
3.1找出高效益的量化項目集 20
3.1.1找出高效益與弱效益的量化項目集 20
3.2產生量化項目集的條件資料庫 24
3.3高效益的量化關聯演算法 27
4.實驗比較 31
4.1人造資料產生方式 31
4.2效能評估與比較 39
4.2.1項目個數不同之效能評估 40
4.2.2最小效益支持度不同之效能評估 40
4.2.3交易資料筆數不同之效能評估 41
4.2.4演算法之比較 42
5.結論與未來工作 45
6.參考文獻 46

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