隨著市場資料的大量增加，從資料庫探戡出有用和有意義的關聯法則已經成為一個重要的研究課題。過去的做法著重在資料的屬性，在這篇論文裡，我們考慮交易本身的屬性來將市場資料組織成一棵具有多重屬性的階層樹，並導出其對應的關聯法則。再者，我們進一步的讓使用者經由這些屬性指定查詢條件，由推導出的關聯法則之間挖掘出有趣的相關性質。最後，我們進行實驗來評估並比較其效能。

With the growth of a large amount of marketing data, mining useful and meaningful association rules from databases has become an important research topic. Previous works have focused on the attributes of the marketing data to derive association rules. In this paper, we consider the attributes of transactions and allow users to specify queries against the attributes and then discover the interesting correlations among the derived association rules. For efficiency, we organize the marketing data as a multiple-attribute hierarchical tree by the attributes of transactions to derive the corresponding association rules. Finally, we make experiments on a synthetic database for performance evaluation and comparisons.

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