First of all, thank you very much for sharing your excellent work and codebase. While using your kmean_features clustering function, I encountered an issue I’d like to kindly ask about:
Even when the input features remain identical and a fixed random_state is set, the number of unique clusters in the KMeans output labels is sometimes less than the specified n_clusters=100 (e.g., 95 or 98). This suggests that not all 100 clusters are actually populated.
My questions are:
1.Did you observe this same behavior during the development of the project?
2.Do you have any recommended approaches to ensure that all 100 clusters are consistently utilized?
Thank you again for your valuable contribution to the community. I greatly appreciate your time and guidance.
First of all, thank you very much for sharing your excellent work and codebase. While using your kmean_features clustering function, I encountered an issue I’d like to kindly ask about:
Even when the input features remain identical and a fixed random_state is set, the number of unique clusters in the KMeans output labels is sometimes less than the specified n_clusters=100 (e.g., 95 or 98). This suggests that not all 100 clusters are actually populated.
My questions are:
1.Did you observe this same behavior during the development of the project?
2.Do you have any recommended approaches to ensure that all 100 clusters are consistently utilized?
Thank you again for your valuable contribution to the community. I greatly appreciate your time and guidance.