🦉 🎤   RapidMiner Wisdom 2020 - CALL FOR SPEAKERS   🦉 🎤
We are inviting all community members to submit proposals to speak at Wisdom 2020 in Boston.
Whether it's a cool RapidMiner trick or a use case implementation, we want to see what you have.
Form link is below and deadline for submissions is November 15. See you in Boston!
Preparing data for pattern recogintion
Currently I am writing my master thesis in electrical engineering. I guess using rapidminer fits perfectly for some excellent simulating results. I guess I could write something about the simulation in my thesis. But first of all here some information about my data: I have a database resp. a set of training data, that looks like this:
There are multiple containers (1..n). Each container has multiple measurements (1..m). Each measurements consists of x-y-z data. The first measurement can have 100values for each component x,y and z. The second measurement might have 130 values for each x-y-z component...
container-1 [ measurement-1[x,y,z], measurement-2[x,y,z], measurement-3[x,y,z], ..., measurement-m[x,y,z]]
container-2 [ measurement-1[x,y,z], measurement-2[x,y,z], measurement-3[x,y,z], ..., measurement-m[x,y,z]]
container-n [ measurement-1[x,y,z], measurement-2[x,y,z], measurement-3[x,y,z], ..., measurement-m[x,y,z]]
On the other hand I have on measurement, which will be tested against the training database to classify, if my measurement-x attends to container 1,2, .. n...
My question is, how do I have to setup my CSV or Excel file for the database?! And how can I test a measurement against my database set? I think I have to use x-validation, right? If you need more information about my project, dont hesitate and ask