資料探勘是從現有的數據庫中選出合乎需要的知識、或者值得瞭解的樣式之過程。 近年來，物件概念在多種應用方面深受歡迎且廣被使用，尤其適用於複雜的資料描述。 這篇論文針對如何從二元的物件導向交易資料、及網站伺服器的記錄中找出相關知識提出兩種新演算法， 也同時利用數值化物件導向的交易資料、及上述網站伺服器的記錄中提出兩種模糊資料探勘演算法。
在應用上，這篇論文嘗試在無線網路中發現模糊個人行動樣式以幫助系統提供個人化服務。其將每個行動用戶在各地方區域的到達時間及所停留的時間當成重要的屬性來表示所得結果，由於到達時間及停留期間為數值型態，所以利用模糊概念來處理它們以形成語意項目。此外，本論文亦提出一個基於AprioriAll演算法的模糊探勘演算法，但是有許多針對行動網路的特殊考量和AprioriAll演算法不盡相同，這些差異造成在設計演算法時需有更細緻的考量。

Data mining is a process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. This thesis thus proposes two novel data-mining algorithms for extracting interesting knowledge from transactions stored as object data and logs of object-oriented web server. We also proposed two novel fuzzy data-mining algorithms for extracting interesting knowledge from quantitative transactions which are stored as object data and log of quantitative object-oriented web server.
In applications, this thesis attempts to discover fuzzy personal mobility patterns for assisting systems in providing personalized service in a wireless network. The arrival time and the duration time of each location area visited by a mobile user are used as important attributes in representing the results. Since both the arrival time and the duration time are numeric, fuzzy concepts are used to process them and to form linguistic terms. A fuzzy mining algorithm has then been proposed, which is based on AprioriAll algorithm, though different in several ways aspects. The difference arises more delicate considerations in the design of the proposed algorithm.

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