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.
I want to visualize these data into 2-dimensional spaces. So my steps are like these.
- PCA process
=> let each datum get x, y coordinates.
- Clustering
=> DBSCAN, K-means, etc., something like those.
I've heard that the proportion of variance is important, and I have the following proportions:
Importance of components: 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
(PC1's PV: 0.2009, PC2's PV: 0.1401)
So, when I convert data into 2-dimension space, as far as I've understood, I think I should project data into (PC1, PC2) coordinates, which only has 0.3410 (Cumulative Proportion)
Isn't 0.3410 (a slightly lower value than I'd expected) too unreliable for that data positioning? Also, is there other way to project that data into 2D space that has more cumulative proportion?
