In the context of time series data mining, I have read about time series segmentation and time series clustering, but I couldn't differentiate between both. In case they are different, how these methods are correlated with each other?
Well from my understanding (please correct me if I am wrong), the segmentation is considered as a preprocessing step for the clustering phase. I mean that the segmentation step is used mainly to partition your time series data into segments, let's say into states. After that, a conventional clustering algorithm can be applied to group these segments into clusters (similar segments belong to the same cluster).
As an example, let's say that the segmentation process represents a given time series into the following segments: (S1, S2, S3, S4, S5, S6). Then after applying the segmentation process, a conventional clustering method is applied to cluster the extracted segments. So we might end up with something like this: If k = 3: then K1 {S1, S5}, K2 {S3, S6}, K3 {S2, S4}
Please correct me if I am mistaken, and provide links for more clarification if you have any. Thank you
Segmentation takes one time series, and splits it into segments.
Clustering takes many time series, and aggregates them into clusters.