# clustering before or after PCA?

I'm newbie into data science, and I had some problems dealing with my project. I'm trying to visualize multidimensional data into 2D after clustering with using a lot of methods. (kmeans, DBSCAN, OPTICS, agglomerative, spectral...)

I have multidimensional data. (11 columns - attributes , 150K rows - number of data). It is slightly sparse-alike data, for example, which means one datum has numeric values like (0, 0, 6.5, 0, 0, 7.5, 0, 0, 4.5, 0, 0)

So, each datum has approximately 2~5 non-zero attribute values...

Below is not exactly same with my project, but it's similar.

https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html

But, as I'm new to this, I am curious about the sequence of PCA and Clustering. I think there are 2 scenarios.

[1. Do clustering before PCA]

That means, I am using PCA just for visualization. But I have a question. In that case, If I process clustering with raw data, are all clustering algorithm (mentioned above) fit to my data type well.

[2. Do clustering after PCA]

In this case, I have other problems. My data's importance of components are like below.

                          PC1    PC2    PC3    PC4     PC5     PC6     PC7    PC8     PC9    PC10
Standard deviation     1.4173 1.1836 1.1141 1.0108 0.99109 0.95231 0.89091 0.8456 0.71542 0.64610
Proportion of Variance 0.2009 0.1401 0.1241 0.1022 0.09823 0.09069 0.07937 0.0715 0.05118 0.04174
Cumulative Proportion  0.2009 0.3410 0.4651 0.5673 0.66551 0.75620 0.83558 0.9071 0.95826 1.00000


As far as I've understood about visualizing multivariate data into 2D, I have to choose 2 PCs.(e.g.> PC1, PC2). However, my data's PV is slightly lower than I'd expected.

So, is it okay if I choose (PC1, PC2) to coordinates to be clustered and process clustering? Also, can I choose other PCs (e.g. PC5, PC8) to coordinates to be clustered?

• If you have many values that are zero, you might not get good results from the clustering methods. Imagine you have just two dimensions and most object (rows) have one 0 somewhere. Many objects would end up on one of the axis in your real coordinate space. Is this what you want to cluster? The same problem (but in multiple dimensions) applies in your case. You might have to think, whether you might define a metric that takes this into account and then apply clustering using this metric. The metric gives you are "similarity matrix" and you get use this for clustering (see your own link). Feb 1, 2020 at 11:55