本研究考慮多門檻限制以及產品的銷售期間，採擷跨階層間在各產品銷售期間的關聯規則。 考慮多門檻限制的跨階層關聯規則採擷是相當重要的研究。然而他們尚未考慮到多門檻限制以及產品生命週期或是產品銷售期對於大型資料庫關聯規則採擷的影響。以往對於多門檻限制採擷單一階層的方法有MSApriori,其能夠讓使用者依照產品實際販賣次數來設定各產品的門檻值。然而在零售店內有許多產品並非是整年都有販賣，因此我們應該要考慮各產品在其銷售期間的交易紀錄來計算support值。過去以往的研究大部分採用Apriori延伸出來的方法，稱之為CL_Apriori，以進行跨階層的關聯規則採擷。然而CL_Apriori 演算法主要是依由上而下且逐層的方式進行採擷，例如 MMS_Cumulate。不過在過去的方法不僅在採擷的效率上不彰，更無法採擷出完整的跨階層關聯規則。 本研究提出一個以FP-tree演算法為基礎加入了多門檻限制的設定，由下往上同時合併考量的方式來採擷跨階層的關聯規則的演算法，稱之CL_FP-tree(MIS)。此方法能夠改進以往考慮多門檻限制採擷大型資料庫的跨階層關聯規則的效率問題，也能夠得到更多更有趣的潛在關聯規則。我們延伸林宏韋(2003)計算跨階層產品銷售次數的方法，加入了計算各產品銷售期來計算各產品support值的方法。 我們利用實際的資料驗證CL_FP-tree(MIS)的確比CL_Apriori(MIS)更有效率，同時CL_FP-tree(MIS)也能夠採擷更多的跨階層關聯規則。同時，CL_FP-tree(MIS)也解決了以往採擷大型資料庫時，因為資料庫越大但是1-frequent items的數目越少的狀況。 Cross-level association rules mining with multiple minimum supports is an important generalization of the association rule mining problem. Instead of setting a single minimum support for all items, Liu et al. proposed a method, named MSApriori, to allow users for specifying multiple minimum supports to reflect the natures of the items. Because not all items are sold in a whole year, we should consider the transactions in the sold periods of items as we calculate the supports of items. Previous techniques for mining cross-level association rules with multiple minimum supports are most top-down, progressive depending method extended from Apriori algorithm, e.g. MMS_Cumulate. Previous approaches result in worse mining efficiency and incompleteness of mined rules. In this research, we propose a bottom-up, simultaneously merging method based on CL_FP-tree, called CL_FP-tree (MIS), to improve the mining efficiency and completeness of mining cross-level association rule with multiple minimum supports. We extend the procedure, which proposed by Alex H.W. Lin (2003) for supports counting, not only to count the supports for all items, but also to judge the sold periods of all items as the basis of support counting. CL_FP-tree (MIS) aims to reduce the number of database rescans for finding the cross-level information. We implement the CL_FP_tree (MIS) with real data and find the results that the efficiency of CL_FP_tree (MIS) is better than CL_Apriori(MIS). And the number of cross-level association rules found by CL_FP-tree (MIS) algorithm is more than CL_Apripri(MIS). Furthermore, we solve the problem that the number of 1-frequent items decreasing as long as the number of transactions increasing.
