X Second samples taken on unevenly spaced intervals
It depends how you want to cluster the data, but here are some options....

FEATURE ENGINEERING
You could, for example, completely ignore the timestamps and just seek
to cluster the different modes of operation are based on the
magnitude of the feature alone. Here, simply extract the values into
a new feature list.
Otherwise, you have to consider what is important about the samples. For example, does the fact that one snapshot containing 5 samples vs 8 samples distinguish it between modes? If so they, build your feature vector based off counting the number of samples in a day.
In doing so, you will create single features for each day from various attributes in the day (e.g. magnitude, number of samples), enabling you to cluster them based off these features.

RESAMPLING
Conversely, you could resample all the data into uniform intervals, but this would probably need lots of assumptions, and would therefore introduce noise.