I've got multiple datasets containing measurements collected at different nodes in the real network. Each dataset is associated with one node.
Because some nodes have got similar properties and their datasets follow a similar trend, I would like to group the datasets into clusters. Each cluster should contain all datasets with a similar trend.
As per my understanding, standard clustering techniques usually apply to a single dataset and group data points of a single dataset. How can i apply clustering like k-means to my scenario without tearing datasets apart (i.e. without datapoints from one dataset being added to different Clusters)? I am thinking of finding datasets meta features like statistical features of each data sets and cluster them based on these features. Is there any other way you think more appropriate?
Each dataset in my case has fewer samples in order of few hundreds.