改良屬性導向歸納法（AOI）挖掘多值（Multi-Values）屬性資料演算法之研究

A Study on the Modified Attribute-Oriented-Induction Algorithm of Mining the Multi-Value-Attribute Data

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，雖然傳統AOI方法用在廣義特徵的尋找非常有用，但它只能挖掘單值屬性資料的特徵，如果資料具有多值屬性，傳統的AOI方法就無法找到資料的廣義知識;另AOI演算法須以建立概念階層為歸納依據，不同的分類原則，或不同的分類值，其所得出的概念樹即不同，影響歸納的結論，基於這個問題，本論文提出一種結合化簡布林數值的卡諾圖（Karnaugh Map）之改良式AOI演算法，不需建立概念樹，並可以處理多值屬性的資料，找出其中各屬性間隱含的廣義特徵。

Attribute Oriented Induction method （short for AOI）is one of the most important methods of data mining. The input value of AOI contains a relational data table and attribute-related concept hierarchies. The output is a general feature inducted by the related data. Though it is useful in searching for general feature with traditional AOI method, it only can mine the feature from the single-value attribute data. If the data is of multiple-value attribute, the traditional AOI method is not able to find general knowledge from the data. In addition, the AOI algorithm is based on the way of induction to establish the concept hierarchies. Different principles of classification or different category values produce different concept trees, therefore, affecting the inductive conclusion. Based on the issue, this paper proposes a modified AOI algorithm combined with a simplified Boolean bit Karnaugh map. It does not need to establish the concept tree. It can handle data of multi value and find out the general features implied within the attributes.