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.

1. PCA process

=> let each datum get x, y coordinates.

2. 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?

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.

1. PCA process

   => let each datum get x, y coordinates.

2. 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?

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.

1. PCA process

=> let each datum get x, y coordinates.

2. Clustering

=> DBSCAN, K-means, ... sth like those.

I'm newbie into PCA, but I've heard that the Proportion of Variance is important, but I have below following Proportions. (I got it with R programming)

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 (slightly low 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?

Sorry for my bad background knowledge and English. I've struggled to find out by myself, but it is difficult.

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.

1. PCA process

=> let each datum get x, y coordinates.

2. 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?