屬於潔淨能源的生質能具備了環保的概念與優勢，它可透過生物轉換的方式得到可燃性的氣體，此轉換關鍵在於微生物的選擇，而古生菌（Archaea）能在極端的環境下生存，並且可透過基因的代謝來釋放能量，具備這些性質使得古生菌有助於生質能的轉換，此外古生菌主要包含了甲烷菌、嗜鹽菌、極端嗜熱菌三大類。
這些古生菌對於生質能轉換的幫助所呈現的效果優劣並非一致，然而，為了要從這三大類的古生菌中選出符合生物轉換條件的菌種，且考量到實驗時篩選的時間與成本，因此本文嘗試提供群聚分析的方法作為初期的篩選工具。方法方面，則是使用模糊群聚分析法與階層式群聚分析法，並選擇27株古生菌的基因組，即密碼子使用偏向數據做為分析的資料量與變量，最後經過實例驗證，這兩種群聚分析法，可以達到篩選出最適的古生菌，而這些古生菌在性質上符合了生質能轉換的條件。
上述方法中模糊群聚分析的優勢為可以從少量的資料發掘出有用的資訊；另外，階層式群聚分析的優勢則是擁有可以快速掌握分群狀態的樹狀結構圖，它們共同的特點是：皆以亮胺酸的群聚結果與生物學上的分類相吻合、演算過程快速穩定、分群明確。
本研究所提出的方法，可達到篩選的目的，而被探索出的菌種，皆可被應用於生質能轉換，因此藉由此方法，不須透過繁雜的實驗，即可得到適合的菌種，所以本文的方法可以作為實驗的前置作業，在效益方面，除了提升解析上的效率，也可以降低成本；此外，本研究發現了胺基酸與生質能之間，具有相對的影響力與關係。

Biomass energy is a type of clean energy that can produce inflammable gas through biological conversion. This conversion depends on the choice of microorganism. Archaea are microbes that can survive in extreme environments and that release energy via genetic metabolism. These properties make Archaea useful for biomass energy conversion.
The Archaea, however, are made up of three major categories, methanogens, halophiles and extremophiles, that produce varying effects when used for biomass conversion. In order, then, to more accurately single out the most effective organisms for such conversion while also considering the time and cost involved in such selection, this article attempts to provide a method of cluster analysis as an initial screening tool, methods. the use of fuzzy c-means and hierarchical clustering analysis, we selected 27 Archaea and the codon usage of their genomes as items and variances for analysis, finally, after verification instances, these two cluster analysis method, both cluster analysis method can achieve optimal screening Archaea, and these Archaea in nature in line with the conditions of biomass energy conversion.
Where fuzzy c-means, can dig out useful information from the data of a little amount. The results show that the methodology used was effective for initial selection, simplifying the experimental process, increasing its efficiency, and lowering its cost. In addition, there are advantages to generating dendrograms and agglomerative coefficients in the hierarchical clustering analysis method, we used dendrograms to identify the clustering status of organisms and to determine their kinship based on agglomerative coefficients. Their common features are: （1）all match by the amino acids Leu clustering results and biology for classification. （2）calculation process is fast and stable. （3）clustering explicit. Comprehensive analysis of the results, the method presented in this paper, without going through a complicated experiment, can immediately achieve the purpose of initial screening.

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