在模糊聚類分析中，有很多模糊聚類的演算法，模糊c均值（
Fuzzy c-means）聚類演算法是比較廣泛被使用的方法，在很多的文獻中，FCM演算法被做的許多推廣，因此產生的許多模糊聚類演算法，例如：RFCM，CFCM，AFCM，PFCM，PIM，ICS，MEC，FRC及FGcM演算法等等，都是FCM的推廣。
在這篇論文當中，我們要討論的是由FCM推廣的FRC（Fuzzy
Realtional Data Clustering）演算法在體適能上的應用，首先我們用體適能測驗中，20歲到65歲坐姿體前彎測驗的常態型模糊資料，運用LR-類型模糊數的結論去定義一個矩陣 ，再以FRC演算法對矩陣 做聚類分析，然後用MPC指標來判斷哪一個聚類結果較好，再跟醫學資料的分析做比較，來判斷FRC演算法的
聚類結果是否合理。

In fuzzy cluster analysis, there are many fuzzy clustering algorithms.
The fuzzy c-means (FCM)clustering algorithm is the most widely used method. In many literatures, FCM clustering algorithm produces many fuzzy clustering algorithms,such as relation dual of fuzzy c-means
(RFCM),conditional fuzzy c-means(CFCM),alternat　i　ve fuzzy c-
means (AFCM), penalized fuzzy c-means(PFCM),partition index max imization(PIM) ,inter-cluster soparation(ICS),maximum entropy-based
clutering(MEC) ,fuzzy relational dataclutering(FRC) and fuzzy generalized
c-means(FGCM) develop algorithm,etc.
In this paper, we apply FRC(Fuzzy Realtional Data Clustering)to the analysis of the data of sit and reach tests of Physical Fitness and compare the results with its original data. First,we use the real data from sit-and-reach tests of Physical Fitness of the people in Taiwan between 20 years old to 65 year old to define a metric which base on LR-type fuzzy numbers. Then we use FRC algorithm to compare and we consider which result is better by MPC index. At last,we
investgate the differnces between the results and Medicine data.

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