CBR(Case-Based Reasoning)為現今建置專家系統時被廣泛使用的技術，其特色為當目標領域缺乏固定可使用的規則與隨時可諮詢的領域專家時，系統設計者可透過所擁有的案例與資料，對其屬性特徵進行分析與整理，依其有興趣的項目結果，建立一套查詢系統。當使用者得到新案例，希望能夠由過去存在的案例中得到參考資訊時，便可使用此查詢系統經由案例比對，得到推薦的答案和相關資訊。

　　然而採用傳統的CBR方式時會遇到兩個問題： （一）當現存案例庫的案例數量龐大時，案例查詢若採用逐一比對的方式，將花費相當多的時間；（二）原始案例擷取時所建立的特徵與屬性，對於該系統中案例的重要性也不盡相同，若將其重要性一視同仁全部比對，會對查詢結果的準確性與時效性有所影響。本研究將提出一建立Case-Base案例庫的架構，第一階段先經由分群技術，將所擁有案例庫中的案例，依其相似的程度進行分群，如此進行查詢時將可不用對所有案例全部比對，可以先判斷該案例的可能查詢結果是屬於哪一群，後再對該群所屬的案例進行比對；第二階段在使用基因演算法，對所有特徵屬性進行權重的設定，找出各屬性的重要度，以提升查詢時的準確度。透過上述兩階段方式，將使得此系統擁有較好的查詢速度，同時保持原有的查詢準確性。

　　CBR(Case-Based Reasoning) is a common used technique of developing modern expert system. Its special feature is that system developers would be able to build query systems through analysis and organize the attributes of datasets, when they could not find domain experts or regularly consultants. With the aid of such systems, users can get relevant information and proposed answers from old data, by insert attributes of new cases they received.

　　However, traditional CBR has two major problems: (1) query time expands along with the quantity of the cases stored in database. Scan through each cases for every individual query would be time consuming. (2) The importance of the attributes the cases differs. If all attributes treat the same, the accuracy of proposed answer would be affected.

　　This research proposes a CBR framework of two stages. At the first stage, using clustering algorithm to separate all cases into several clusters by comparing the similarities of each cases. That the system would only have to scan through the most similar cluster of the queried case, without going through all cases of the database. Second, set up weights of each attribute for each cluster using genetic algorithm, these weights indicate the importance of the attribute. With the aid of these weights the proposed answer will be more accurate. Through these two stages, the system build after will have better performance in processing time, and keep the accuracy high as scan through all cases

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