隨著大數據時代來臨，機器學習方法已然成為熱門學習的主題，主要分為監督式學習與非監督式學習，亦即分類與分群。本研究以羅吉斯迴歸配適結果加權距離矩陣，以資料雲幾何樹分群法為主，在含有類別變數的紅酒資料中，透過先分群再分類的方式，判斷是否可以得到更佳的預測結果。並比較監督式學習下各種機器學習方法預測表現，及非監督式學習下後再透過分類器方法的預測表現。在內容的排序上，首先介紹常見的分類與分群演算方法，並分析其優缺點與假設限制，接著將介紹資料雲幾何樹演算法，並詳述執行步驟。最後再引入加權資料雲幾何樹演算法，將權重的觀點應用在資料雲幾何樹演算法中，透過紅酒資料，比較各種分類與分群方法的預測準確率。

Machine learning has become a popular topic since the coming of big data era. Machine learning algorithms are often categorized as being supervised or unsupervised, namely classification or clustering methods. In this study, first, we introduced the advantages, disadvantages, and limits of traditional classification and clustering algorithms. Next, we introduced DCG-tree and WDCG algorithms. We extended the idea of WDCG to the cases with label size=3. The distance matrix was modified by the fitted results of logistic regression. Lastly, by using a real wine dataset, we then compared the performance of WDCG with the performance of traditional classification methodologies. The study showed that using unsupervised learning algorithm with logistic regression as a classifier performs better than using only the traditional classification methods.

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