Comments (1)
In my opinion, no, because the definition of cluster quality is subjective and domain specific. You'll need to run TICC multiple times to get different assignments and see which works best for you. I've done this with different values of number_of_clusters
and then compared how they break down .. to some extent comparable to hierarchical clustering. When interval boundaries started to change just from adding more clusters I concluded that it was overfitting.
This little tool might be useful to illustrate your cluster assignments: https://gpcrviz.github.io/ChangePointDetection/DisplayIntervals/
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Related Issues (20)
- What does cluster_MRFs represent?
- Unexpected behavior for the BIC score
- ValueError: operands could not be broadcast together with shapes (16,) (32,)
- 1d case HOT 1
- Scipy >= 1.3 causes TICC_solver.py to hang HOT 1
- Runtime Error for loading SNAP Module , while running car.py HOT 1
- Reports are generating with inconsistent and getting mismatch for the Custom datasets. HOT 1
- strange results - 1 var time series
- How should the “val” be modified? HOT 1
- cvxpy and snap keep reporting errors
- Code environment configuration requirements
- difference between TICC_solver.py and TICC.py in paper_code folder?
- difference in the optimization of Graphical Lasso between the paper_code TICC.py and in TICC_solver.py
- How to use the trained model on unseen data HOT 2
- Why it gives different result if I shuffle the sample data? HOT 1
- smoothen_clusters: LLE of last points not computed ?
- how to get betweenness centrality for each sensor based on MRF network?
- stack_training_data 函数好像有问题
- Including cluster mean as a cluster feature
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