I have a longitudinal dataset for 500 sensors reading at 25 time point.
I want to cluster those sensor readings at each time point , from T1 to T25
bur I want to know which cluster each sensor lies in at each time point in easier manner.
Assuming I have found 3 clusters in T1, and 2 in T2, is there a way to measure if sensors in cluster 1 T1 significantly similar to sensors in T2 cluster 1 and 2?
Another question, which machine learning clustering algorithm are good for my case? which one don’t require hyper parameter tuning ( like maybe defining the number of cluster ) ?
last, any visualisation method to print clusters per sensors by time ?
I think that you can use cluster multiple time series using K-mean. To obtain the number cluster, you can use the Elbow method or silhouette score. You can also use Dynamic Time Warping (I did not use this technic but it is used with data that have similar shapes).
Thank you Inès François for your reply.
I think this will cluster the sensors by their progression by time, but how to cluster the different states that sensors will pass through by time?
Thank you again dear Inès François
I didn't use sequence plot previously.. have you tried that?
I am asking because i want to know if i can extract the used colours in this plot ( which will be the aimed states in my case) .. or i have to re-cluster the images of these plots and utilize from get-colour function to identify the used colours ..
Actually, i thought about that since i might end up in more than 5 time series clusters ..
So, in this case does sequence plots use same colour for the number that will be plotted? or each plot will be populated with different colour for each number?
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