本研究利用叢集分析的方法融入特徵萃取裡，以減低在高維度資料中存在著一些 同類別差異性、非常態或多峰混合分布以及共同平均值等這些情況使得辨識率下降的 問題，從數據上來看可以發現在PCA 中因為沒有考慮到組間分散度矩陣以及組內分散 度矩陣的問題，因此融入叢集分析的方法並無法獲得相當地改善。對於 LDA 以及 NWFE 兩種特徵萃取法，因為兩種方法都是要最佳化組間分散度矩陣及組內分散度矩 陣的比率，所以採用融入叢集分析的方法通常能獲得效能上的增加以及辨識率的提升。
單就特徵萃取的方法來比較，可由數據上明顯的看出只用NWFE 本身的辨識率已 經相當高了，即使是融入叢集分析之 LDA 所萃取過後的辨識率，也難以比單純使用 NWFE 萃取過後的辨識率高。
此外在本研究中所使用了k-mean 及 fuzzy c-mean 兩種叢集分析的方法，對於本研 究中的效能兩種叢集分析方法就數據上來看並無太大的差異。另外，在 fuzzy c-mean 的演算法中還有提供一些對於使用叢集分析的資訊量在本研究中並無使用到，因此希 望往後的研究能夠多加利用這些fuzzy c-mean 所提供的訊息。
高維度資料中同類別也許可以分成數個叢集，但是在本研究中採用每個類別固定 分成數個叢集。在未來的研究中期望能找到一個準則，希望能夠有一個機制能自動的 判斷該類別分成幾個叢集對整體分類的效果能夠達到最大的改善。
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