In clustering ($K$ means, for example) when I have $N$ features and after creating the model (with this $N$ features) to visualize this model I need to reduce this $N$ dimensions into $2$ or $3$ dimensions, let's say I will use (PCA) fro example.
My question is how I can analyze the results (grape with principal component)?
this is simple example :
Data without reducing : AGE,GENDER,SPENT,SALARY,CAR,.....
Data after reducing dimension:
Principal Component $1$, Principal Component $2$, Principal Component 3
what do PC1,PC2,PC3 means