Clustering with groups in data related to cluster label I want to predict which device got used in which room. Therefore I've got device and sensor data.

My idea was to create a feature vector lie this:

             ----------------------------------------------------------
Data-Vector: | u_1 u_2 u_3 | x_1 ... x_7 | y_1 ... y_12 | z_1 ... z_4 |
             ----------------------------------------------------------
 Categories: | device_data | room 1 data | room 2 data  | room 3 data |
             ----------------------------------------------------------


My device data contains amongst other things:
+ timestamps when the device got turned on/off
+ average power consumption and divergences

My room data contains for example:
+ sensor data of motion detector and timestamps
+ sensor data of lamps (turned on/off) and timestamps
+ weather data

In the feature vector I've got the room data closest to the turn on/off timestamp.

All data points itself are floats.

My idea was to use k-means for clustering.
My problems are:
1. When using k-means, how can I tell which cluster correlates to which room label (room1, room2 or room3)?
2. I think it could be beneficial if I add somehow the information: which sensor is which room.

Can I manipulate the data in k-means algorithm so it will only consider:
the device data and room 1 data for the first cluster and sets everything else to zero
the device data and room 2 data for the second cluster
and so on...

This way I could tell that cluster x correlates to room x.

Or will this somehow break the k-means algorithm?
If you have apriori known labels, then use the labels, not clustering.

Clustering is quite difficulty and fragile, so always prefer a simpler solution if you can.

It's not very clear what you are trying to solve. Select columns to split the data into rooms? Just select the columns then.