近來有關決策制定的研究，多數是以資料探勘（data mining）或機器學習（machine learning）的理論為基礎，使專家系統能透過學習樣本（training sample）來產生決策規則，達到提高決策精確度和降低系統維護成本的目的。
傳統進行模糊分類（fuzzy classification）的作法，大部分是對模糊程度值（fuzzy degree）採截斷（crisp-cut）的方式，來達到決策制定的目的，因此它們雖能產生複合型態的決策規則，但卻無法獲得決策結論的模糊關係度函式。有鑑於此，在本論文中，我們提出一種藉由學習樣本（training sample）來推導模糊決策規則（fuzzy decision rules）的方法，稱為模糊近似推論程序（fuzzy approximation reasoning method）；本程序能同時滿足以下兩項需求：一是能推導出決策結論的模糊關係度函式（fuzzy membership function），二是能產生複合型態（disjunction-conjunction）的決策規則。
在模糊近似推論程序中，我們運用所設計之相依度函式（dependency-degree function）評估並找尋與決策結論（conclusion）相關的模糊屬性（fuzzy attributes），再透過這些模糊屬性的結合，產生決策結論的模糊關係度函式，以完成制定模糊決策規則的目的。此外，模糊近似推論程序亦能運用在模糊關聯法則（fuzzy association rules）的推導。因此，我們提出近似歸納程序（approximation inducing method）的演算法來探勘模糊關聯法則。

Most fuzzy classification systems proposed before applied a crisp-cut approach on the fuzzy degrees of the fuzzy attributes and conclusions to generate decision rules. Although, by the crisp-cut approach, decision rules with conjunction-disjunction form can be derived from training-samples, the membership functions of the conclusions cannot be generated. In this paper, a learning method named Fuzzy Approximation Reasoning Method is proposed. Two requirements can be satisfied by the method：(1)deriving fuzzy decision rules with conjunction-disjunction form from training-samples, and (2)generating the membership functions for the conclusions.
In Fuzzy Approximation Reasoning Method, the dependency-degree function is designed for estimating the relationship between a conclusion and the fuzzy attributes. For the fuzzy attributes related to the conclusion, their membership functions will be combined to construct the membership function of the conclusion such that the associated fuzzy decision rule is derived. Moreover, the Fuzzy Approximation Reasoning Method also can be used to mine fuzzy association rules. In this paper, the Approximation Inducing Method is proposed to demonstrate how to mine fuzzy association rules by applying the Fuzzy Approximation Reasoning Method.

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