模式樹的樹狀結構與決策樹類似，不同之處在於每個葉部節點是存放著一條線性歸式，以用來預測類別值。在處理數值預測的問題時，模式樹是一種不錯的方法，GUIDE為近年來在效率和精確度上都表現不錯的模式樹演算法，其主要是採用統計裡的卡方檢定來挑選節點屬性，而本研究提出的KCMT，是利用K-means分群法將資料點分群後，依據屬性的區隔能力來挑選節點屬性，並且提出三種不同找尋屬性分割點的方法，為2個標準差法、MSE法、歸類正確率法，分別記為KCMT(2sd)、KCMT(mse) 和KCMT(c)。從本研究的測試結果來看，利用分群資訊來找尋屬性分割點的方式會具有較好的學習結果，至於未修剪前的樹狀結構大小方面，KCMT大多比GUIDE來得小，而修剪後的樹狀結構大小反倒是GUIDE來得小，由預測精確度來看，利用歸類正確率法來找尋屬性分割點的方式在各資料檔中的平均表現是較佳的，而KCMT(c)與GUIDE的表現是優劣參半、不分軒輊，因此整體而言，KCMT(c)的學習成效略優於GUIDE，使得KCMT(c)的表現和GUIDE相比是具有競爭力的。此外，KCMT是採用和M5相同的處理名目型屬性的做法，由大多資料檔的測試結果顯示，當由名目屬性轉換成的二元屬性個數不是很多時，這種處理名目屬性的方式對於KCMT的學習結果並沒有太大的影響。

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陳子立，“結合特徵選取與判定係數以建構模式樹之方法”，碩士論文，國立成功大學工業管理科學研究所，民國92年。
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