text: Title: [ENH] window based time series segmentation via clustering
  Body: Two advanced forms of https://github.com/sktime/sktime/issues/6750:
  
  Non-overlapping
  
  1. run a non-overlapping sliding window across the time series to get subseries and segments
  2. apply a *time series clusterer* to the pooled set of all the subseries.
  3. For each segment, the assignment is the cluster assignment of the segment it is in
  
  Parameters: the clusterer; the sliding window schema
  
  Overlapping
  
  1. run a non-overlapping sliding window across the time series to get subseries and segments
  2. apply a *time series clusterer* to the pooled set of all the subseries.
  3. Each point will now be a part of multiple segments. We can do two things:
     * 3a - aggregate the cluster assignments according to a rule, e.g., majority, or `predict_proba` to output with weights based on the number of segments.
     * 3b - return the overlapping segments
  
  Parameters: the clusterer; the sliding window schema; the aggregation strategy; whether we return overlapping segments, or a non-overlapping segmentation
  