我們的實驗驗證了影像保留高頻及低頻部分時，HOG 特徵更能代表一張影 像，並且對叢集整合近鄰傳播提出了改進方法，包括挑選均一的參考度與做參考 度的倍數調整，使得分群結果進步。
同時我們也對 Single Run 做了深入的分析，指出 Average Link 和近鄰傳播分 別適用於哪些條件的分群，近鄰傳播實驗中不同的參考度取法會對分群結果有什 麼樣的影響，並對幾組不同的資料進行實驗，以驗證我們的論述。
本論文的主要貢獻，在於分析各種分群方法在哪些情況表現較好，哪些情況
較不適用，並提出改進分群的措施。
人臉分群是現今研究廣泛的領域，未來也會是多媒體工程的主要發展方向。
我們探討了一些增進人臉分群的方法，其中近鄰傳播採用均一參考度算是比較少 見的觀點。最後我們想拋出一個問題：如果叢集整合近鄰傳播實驗選用“相似度 矩陣最小的正數值＂矩陣作為參考度，能否讓分群結果更好呢？因為近鄰傳播應 用在叢集整合的研究，算是比較新穎的方向，所以我們的這個猜測，或許不久之 後會有相關的新發現問世。
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