I have a dataset of the number of steps people take throughout a day over a period of months. I aggregated them so that each person will have an average weekday and weekend time series of steps. An example is below:
ID Weekday 00:00 00:30 01:00 ........ 23:00 23:30 A 1 20 30 23 154 256 A 0 15 67 121 35 101 B 1 78 21 46 78 26 B 0 55 301 22 121 451
I have tried clustering using K-means, agglomerative hierarchical and dynamic time warping by combining both weekdays and weekends together (i.e each person only has 1 case of data), and it appears to show 2 distinct groups of people, one group with generally higher levels than the other group.
How can I do clustering while also taking in account whether it is on a weekday or weekend? So for example, there might be a few clusters: one with high weekday activity and low weekend activity, one with high - high, another with low - low, etc?
I am not sure what the terminology is in this case: multidimensional time series? multivariate clustering?