隨著商業趨勢分析應用的需求增加，探勘交易間關聯法則的問題成為資料探勘的重要課題之一。當搜尋的關聯法則不再只是出現在單筆交易的內部，而是橫跨交易之間時，便成為另一項新的挑戰。
過去的研究是用 Apriori-like 的方式以遞增的方式來解交易間關聯法則都問題。在這篇論文中我們提出兩個 graph-based 的演算法分別探勘有條件限制以及最大交易間關聯法則。因為有時候使用者的查詢是『如果今天A股票上漲，那麼可以在後面幾天買哪兩支股票？』像這種有條件限制的查詢，我們希望利用我們提出的演算法可以更有效率的找到符合使用者需求的結果。此外，探勘最大交易間關聯法則其實是達到探勘所有交易間關聯法則的另一個策略。從實驗數據得知，利用我們的演算法找尋較長的交易間關聯法則會比 Apriori-like 要快。在這篇論文中，我們同時探討並分析從多筆交易或多個序列延伸出來的資料探勘的問題，並將之分為五類。

With the growing interest in commercial trend analysis, mining inter-transaction association rules in transaction databases has become an important data mining research. Mining inter-transaction association rules create new challenging problems since they involve date items in multiple transactions.
Previous studies adopt an Apriori-like generation-and-test approach utilized to mine intra-transaction associations. In this study, we propose graph-based algorithms to discover specific large-k inter-transaction itemsets and maximal large inter-transaction itemsets (a maximal large inter-transaction itemset is not a subset of any large inter-transaction itemset.). Most users are interested in some specified rules, hence, discovering specific large-k inter-transaction itemsets efficiently will be helpful to satisfy users' requirements. Moreover, finding maximal large inter-transaction itemsets is another solution for mining all large inter-transaction itemsets. Our performance shows that our algorithms perform well, especially for those potentially long large inter-transaction itemsets. In addition, we analyze the approaches on mining various patterns from transaction or sequence databases. As a result, a classification of this problem into five classes is formed.

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