PCA in Rapidminer

jem810

I would like to determine themes of a corpus of tweets using PCA. I created the process using the ff: read excel,nominal to numeric, PCA and connected the ports. There are no errors but I am not sure on how I can identify the hidden themes using PCA with the standard deviation, proportion of variance and cumulative variance. The proportion of variance ranges from 0-.001. I set the variance threshold at .95.

Can you please help me? Thank you

 

component	std dev	proportion of variance	cumulative variance
PC 1	0.157	0.025	0.025
PC 2	0.137	0.019	0.045
PC 3	0.123	0.016	0.06
PC 4	0.118	0.014	0.075
PC 5	0.115	0.014	0.089
PC 6	0.112	0.013	0.101
PC 7	0.104	0.011	0.113
PC 8	0.1	0.01	0.123
PC 9	0.098	0.01	0.133
PC 10	0.097	0.01	0.143
PC 11	0.097	0.01	0.153
PC 12	0.093	0.009	0.161
PC 13	0.093	0.009	0.17
PC 14	0.092	0.009	0.179
PC 15	0.09	0.008	0.187
PC 16	0.089	0.008	0.196
PC 17	0.087	0.008	0.204
PC 18	0.087	0.008	0.211
PC 19	0.086	0.008	0.219
PC 20	0.084	0.007	0.226
PC 21	0.083	0.007	0.234
PC 22	0.082	0.007	0.241
PC 23	0.082	0.007	0.248
PC 24	0.081	0.007	0.254
PC 25	0.08	0.007	0.261
PC 26	0.08	0.007	0.268

Thomas_Ott

Hi jem810,

 

Have you checked out this post by our partner Simafore? http://www.simafore.com/blog/bid/62911/How-to-run-Principal-Component-Analysis-with-RapidMiner-Part-2

 

He goes in depth on how to use PCA with RapidMiner and how to intepret the EigenVectors 

Thomas_Ott

Hi jem810,

 

Have you checked out this post by our partner Simafore? http://www.simafore.com/blog/bid/62911/How-to-run-Principal-Component-Analysis-with-RapidMiner-Part-2

 

He goes in depth on how to use PCA with RapidMiner and how to intepret the EigenVectors 

jem810

Thanks Thomas!

sebastian_gonza

Is it possible to display the results graphically?

Thomas_Ott

@sebastian_gonza Yes, there is the ability to graph.