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K-Means Clustering (k-means clustering algorithm) belongs to the unsupervised learning algorithm, which can find k different clusters. The centroid of each cluster is calculated by the mean value of the values contained in the cluster. It is easy to accomplish, but may be slow convergence on large data sets.
However, the principal component explains its meaning often with some ambiguity and is not as complete as the original sample. The principal component with a small contribution rate may often contain weight differences for the sample. Therefore, PCA is generally not used for direct feature extraction but for dimension reduction of feature matrices. The results of dimensionality reduction are not ideal for classification
Because the amount of data in this question is not large, and the main purpose is classification, we think that clustering is more suitable for this situation.

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