My dataset have 141 variables, all are numeric. To do clustering based on them, it seems that PCA is required to reduce dimensionality.
The var plot shows that variance among these variables are unstable. ( variance of all variables are under 0.1)
I scaled the dataset, and the var plot shows stability.
Then I do PCA on it and make a scree plot to show the percentage of variance in each components. I was shock that first 10 components only takes up 20% of the total variance (The chosen factors should explain 70 to 80% of variance at least).
I tried PCA without scaling, and still find that first 10 components explain less than 50% of variance.
Does it mean that the PCA is not required, even if the number of variables are large? And do I need to scale the data?