此篇論文中，我們提出一種以區域性為基礎的相似度轉換方法。藉由觀察鏈 結兩端點的區域最近鄰分布，重新調整鏈結權重，使得資料彼此的相似程度能依 照提出的假設做修改，相當於對資料進行前處理。我們分別提出兩種尋找區域性 鄰集的方法：K 最近鄰(K-nn)和可互相包含最近鄰(MI-nn)。實驗結果顯示可互相 包含最近鄰(MI-nn)為基礎之相似度轉換方法能夠尋找任意形狀的群集，且對於 參數的依賴度遠低於 K 最近鄰。我們認為以區域性鄰集為基礎的相似度轉換方 法能夠凸顯資料之間的邊界，進而提升整體的準確率。
以區域性鄰集為基礎的相似度轉換方法有別於其他相似度轉換方法，不需任 何配對限制的幫助即可達成預期目標。以非監督式分群的角度觀察，與其他非監 督式分群演算法進行比較，對於分布較為特殊的資料集皆可獲得接近完全正確的 結果，顯著優於 K-means、cluto[5]和 DBSCAN[27]；對於蒐集自真實世界的 UCI 資料集，多數的情況亦能獲得相對高的準確率。改以半監督式分群的角度觀察，
由於提出的方法涉及相似度轉換，與 Xing[11]、RCA[12]、 LMNN[16]三種屬於 similarity-based 的半監督式分群演算法更為接近，比較之下準確性都可以獲得相 近的結果，甚至在部分資料集有更高的準確率。
相似度轉換方法未必能完整描述資料的相似關係，因此我們認為將相似度轉 換方法與半監督式分群法結合，可以透過專家的意見彌補相似度轉換所無法涵蓋 的範圍，以提升分群的效能。
除了改善分群準確率外，我們亦能在相似度轉換的過程中縮減資料維度。縮 減維度不僅能加快相似度轉換，同時能在不影響分群結果的前提下，提升分群演 算法的速度。
總結以上實驗結果，此篇論文提出的方法有下列優點：
1. 透過相似度轉換改善分群演算法中所使用的相似度函式。
2. 不需過多的外部參數引導分群。
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3. 在缺少配對限制的情況下，能夠有效的處理任意形狀分布的群集圖形。
4. 將相似度轉換方法與半監督式分群演算法結合，專家的意見能夠彌補相似度 轉換所無法涵蓋的範圍，進一步提升整體的準確率。
5. 我們能透過相似度轉換方法適度的縮減資料維度。維度縮減並不顯著影響分 群結果，亦能減少計算量以提升分群演算法的速度。
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