Sample of N points for hierarchical clustering

UCA-Datalab / nilm-thresholding

:warning: This repository is no longer actively maintained. It previously dealt with Non-Intrusive Load Monitoring (NILM), focusing on predicting household appliance status from aggregated power load data. We explored different thresholding methods and evaluated deep learning models for regression and classification tasks.

https://doi.org/10.1007/s11227-023-05149-8

34 stars 6 forks source link

Sample of N points for hierarchical clustering #12

Open daniprec opened 3 years ago

daniprec commented 3 years ago

Hierarchical clustering does not work for too many points (>1e5). We need to take a sample of N power load points per appliance (N=1e4). It is necessary to find a method that ensures the sample of N points contains at least one activation of each potential status. Otherwise, we risk taking a sample of points full of 0's (because many appliances are inactive >90% of the recording period).

daniprec commented 1 year ago

:warning: Notice: This project is no longer actively maintained, and this issue won't be addressed. Feel free to continue the discussion, but be aware that there won't be further updates.