透過安裝於骨盆底肌肉訓練輔助器上的感測器，可以取得如施力、時間等資訊，分析這些資料並將其分群，提供給醫師判斷，賦予其專業認定的特性給各cluster。這些學習後的資料便成為患者的個人化參考依據，讓病人不在醫院時也能正確練習。 本論文的目的在研究分析三類分群演算法，將其運用在骨盆底肌肉訓練的資料分群上。在centroid-based的部份引用了k-means，density-based的部份提出了源自DBSCAN的density-split，而connectivity-based則以SLINK的概念發展了chain。此外，針對noise造成的問題，本論文提出在分群前先排除noise的方法，欲藉此改善分群結果，讓醫師判讀與後續使用的參考資料更為精確。 By attaching a pressure sensor on the device of pelvic floor muscle training (PFMT), we can collect data such as force and time. After data clustering, the proposed system can provide the PFMT data to the doctor. The doctor identifies the cluster by his/her professional knowledge. This identified data becomes the personal training data of the patient. The purpose of this thesis is studying three kinds of clustering algorithms, and implementing it for clustering the data of PFMT. In centroid-based, we reference "k-means". In density-based, we propose "density-split" which is inspired by DBSCAN. Finally, in connectivity- based, we propose "chain" which based on the concept of SLINK. Besides, in order to solve the problem caused by noise. We propose a method that excludes noise before clustering which can improve clustering result, and provide more accurate training data for clinical use.