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.
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?