屬性導向歸納法(簡稱為AOI方法)是最重要的資料挖礦方法的其中一種，AOI方法的輸入值包含一個關連式資料表和屬性相關的概念階層，輸出是任務相關資料所歸納之廣義特徵，雖然AOI方法用在廣義特徵的尋找非常有用，但它只能挖掘關連式資料的特徵，這些資料並不具有序列性，如果資料具有序列性，現有的AOI方法就無法找到廣義的知識，基於這個問題，本論文提出一種以AOI技術為基礎的動態規劃演算法，可以從序列資料中找到廣義的特徵，透過演算法的使用，我們可以發覺一串連續K筆的序列歸納tuples，它可以用來描述K個連續資料區段的廣義特徵，而K值是可以由使用者自行定義。 The attribute-oriented induction (AOI for short) method is one of the most important data mining methods. The input of the AOI method contains a relational table and a concept tree (concept hierarchy) for each attribute, and the output is a small relation summarizing the general characteristics of the task-relevant data. Although AOI is very useful for inducing general characteristics, it has the limitation that it can only be applied to relational data, where there is no order among the data items. If the data are ordered, the existing AOI methods are unable to find the generalized knowledge. In view of this weakness, this paper proposes a dynamic programming algorithm, based on AOI techniques, to find generalized knowledge from an ordered list of data. By using the algorithm, we can discover a sequence of K generalized tuples describing the general characteristics of different segments of data along the list, where K is a parameter specified by users.
