由於資料庫（Data Base）技術的出現，資料量成倍數成長，從眾多資料中挖掘所需知識成為一重要議題，因此不同領域的學者針對不同問題提出許多資料探勘方法，而屬性導向歸納法（Attribute Oriented Induction，簡稱為AOI方法）也於1990年代首次被提出。AOI方法是資料探勘（Data Mining）最重要方法之一，為設定導向的方法，主要用於將關聯式資料庫中的屬性一般化以進行知識挖掘（Knowledge Discovery），此方法的屬性會根據概念樹進行一般化，而概念樹由使用者背景知識設定而成，減少資料庫挖掘的複雜計算。由於傳統的屬性導向歸納法無法判斷何種一般化表格較佳，因此本研究導入成本的概念，將屬性一般化所喪失的詳細度量化為成本，使得結果的優劣能夠根據量化的成本大小判斷，同時，提出概念與AOI方法相似的聚合式階層分群演算法（Agglomerative Clustering） 。此演算法根據成本概念計算資料列兩兩間的合併成本，並找出最小合併成本的兩資料列進行合併，由下而上合併直到滿足終止條件，歸納出較傳統AOI方法更佳的結果。本研究的最後將提出的演算法與傳統AOI方法進行比較，分析在不同資料量及歸納至不同資料列筆數時的表現，發現本研究提出的演算法在不同的情境下，最終歸納表格成本較低，整體表現較佳。;Due to the database technology, it has been estimated that the amount of information in the world doubles every 20 months. Mining information and knowledge from large databases has been recognized as an important issue. Researchers in many different fields have developed lots of solutions in data mining. One of these important methods called Attribute Oriented Induction (short for AOI) has also been proposed in 1990. AOI is well recognized as the most important method of data mining that generalizes attribute in relational databases according to concept trees ascension for knowledge discovery. A concept tree represents the background knowledge for generalization, which applies well-developed set-oriented database operations and substantially reduces the computational complexity of the database learning processes. However, traditional AOI method cannot distinguish which result is better. In this paper, we propose the concept of cost to quantify the losing details when attribute values are generalizing. And we develop an algorithm which combine AOI with agglomerative clustering that is similar to AOI. The proposed algorithm will merge every two tuples and compute the merging cost first, then will find the two tuples whose merging cost are minimized and recursively running the process until the results meets the conditions. Performance studies have shown that the proposed algorithm is superior then traditional AOI.
