Merge sensor data for clustering/neural net usage

I have several datasets i.e. matrices that have a 2 columns, one with a matlab date number and a second one with a double value. Here an example set of one of them

>> S20_EavesN0x2DEAir(1:20,:)
ans =

   1.0e+05 *

   开发者_StackOverflow7.345016409722222   0.000189375000000
   7.345016618055555   0.000181875000000
   7.345016833333333   0.000177500000000
   7.345017041666667   0.000172500000000
   7.345017256944445   0.000168750000000
   7.345017465277778   0.000166875000000
   7.345017680555555   0.000164375000000
   7.345017888888889   0.000162500000000
   7.345018104166667   0.000161250000000
   7.345018312500001   0.000160625000000
   7.345018527777778   0.000158750000000
   7.345018736111110   0.000160000000000
   7.345018951388888   0.000159375000000
   7.345019159722222   0.000159375000000
   7.345019375000000   0.000160625000000
   7.345019583333333   0.000161875000000
   7.345019798611111   0.000162500000000
   7.345020006944444   0.000161875000000
   7.345020222222222   0.000160625000000
   7.345020430555556   0.000160000000000

Now that I have those different sensor values, I need to get them together into a matrix, so that I could perform clustering, neural net and so on, the only problem is, that the sensor data was taken with slightly different timings or timestamps and there is nothing I can do about that from a data collection point of view. My first thought was interpolation to make one sensor data set fit another one, but that seems like a messy approach and I was thinking maybe I am missing something, a toolbox or function that would enable me to do this quicker without me fiddling around. To even complicate things more, the number of sensors grew over time, therefore I am looking at different start dates as well.

Someone a good idea on how to go about this? Thanks

I think your first thought about interpolation was the correct one, at least if you plan to use NNs. Another option would be to use approaches which are designed to deal with missing data, like http://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theory for example.

It's hard to give an answer for the clustering part, because I have no idea what you're looking for in the data.

For the neural network, beside interpolating there are at least two other methods that come to mind:

• training separate networks for each matrix
• feeding them all together to the same network, with a flag specifying which matrix the data is coming from, i.e. something like: input (timestamp, flag_m1, flag_m2, ..., flag_mN) => target (value) where the flag_m* columns are mutually exclusive boolean values - i.e. flag_mK is 1 iff the line comes from matrix K, 0 otherwise.

These are the only things I can safely say with the amount of information you provided.