在行動商務的環境下探勘使用者的行為已成為在資料探勘中一個重要的研究議題。在先前研究當中，已有學者結合了使用者的移動路徑以及購買的交易，來找出行動商務環境下的循序移動樣式。循序移動樣式不只包含了循序購買樣式，還含有對應的移動軌跡。然而，循序移動樣式並不能有效表示在交易資料庫中商品的實際效益值。有鑑於此，本研究的目標為結合行動樣式探勘及效益探勘來找出高效益移動樣式，以改進先前研究之不足。就我們所知，本論文是第一個結合行動樣式探勘以及效益探勘的研究。同時，我們提出兩種不同架構的演算法，分別以階層式架構及樹狀結構為基礎，有效率地探勘高效益移動樣式。除此之外，我們利用一系列的實驗，針對兩種不同架構的演算法做詳細的分析與效能上的比較。實驗結果顯示在不同的系統參數下，以樹狀結構為基礎的方法較以階層式為架構的方法有較佳的效能。

Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns, i.e., the customers' sequential purchasing patterns with moving paths. However, mobile sequential patterns cannot reflect actual profits of items in transactional databases. In this thesis, we aim at integrating mobile data mining with utility mining to find high utility moving patterns. To our best knowledge, this research is the first work that combines mobility pattern mining with utility mining. Two different types of algorithms, namely level-wise and tree-based methods, are proposed for mining high utility moving patterns. A series of detailed analyses and comparisons on the performance of the two different types of algorithms are also conducted through experimental evaluation. The results show that the tree-based algorithms have better performance than the level-wise ones under various system conditions.

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