機器學習法中的決策樹歸納學習法，由於其學習速度快及易於產生明確的知識結構的特性，有很廣泛的實際應用領域。然而其應用層面卻受限於決策樹歸納學習法先天上的限制，較適用於處理名目資料（nominal）符號式的概念。對於連續數值屬性則需要一個決定分割點的演算法或量度標準，將連續數值轉為有限離散值。本研究運用決策樹歸納學習法結合階層式叢集分析，以屬性值相互間的接近程度以及分類結果的相似度作為距離函式進行叢集，來處理連續數值屬性的分割問題，藉著多區間分割方式產生更準確的分類規則，以期能突破決策樹歸納學習法處理連續數值屬性時，舊有應用上的藩籬。本研究採實證方式檢驗此屬性分割方法：實作所提出的演算法，在UCI-ML 測試資料庫上與現有的連續數值屬性分割方法加以實驗比較。以預測準確度與決策樹大小作為決策樹良莠的指標，以驗證本研究所提出的決策樹歸納學習法之有效性。

　　In realized situation, data usually are presented in datasets by both continuous and discrete-valued forms. Not inductive nor connectist learning methods can manage this kind of mixed- form datasets efficiently. While most inductive learning methods can only deal with discrete attributes value efficiently, connectist methods need more
constructing design on datasets which contain discrete-valued attributes. We purpose a new decision tree inductive learning method that uses hierarchical clustering process to construct multi- interval decision rules on continuous-valued attributes. This method occupied two advantages: 1. A decision tree inductive learning method that can deal with “mix-form datasets” efficiently, which means, the datasets that contain both discrete and continuous-valued attributes. 2. Flexibility for the trade-off between the simplification and the accuracy of decision models. The decision tree inductive method was implemented and compared experimentally with known inductive methods. Experiments use ten-fold cross-validation and block design for comparison on ten datasets.

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王錫澤，以歸納學習法求解單元製造系統動態派工問題，國立成功大學工業管理
研究所碩士論文，民國84 年。
吳盈宜，歸納學習法中決策樹連續數值屬性分割點之選擇，國立成功大學工業管
理研究所碩士論文，民國89 年。
邱美珍，決策樹學習法中連續數值屬性之分類研究，中原大學資訊管理研究所碩
士論文，民國85 年。
童冠燁，以歸納學習法探討彈性製造系統動態排程之研究，國立成功大學工業管
理研究所博士論文，民國86 年。
蕭文峰，運用決策樹歸納學習法預測連續數值，國立中山大學資訊管理研究所碩
士論文，民國84 年。