Dec-18-2018, 05:05 PM
Hi all,
I'm a bit of a novice Pythoner and have recently been running a K-Means Clustering in Azure Machine Learning. That method doesn't suit the dataset I have due to noise and I'm thinking that DBSCAN may be the way to go. My question is the dataset is 940 columns by 104k rows so, as a bit of a newbie, I'm not sure of the best way to deal with a dataset this size. Any high level advice much appreciated
Thanks
Mads
I'm a bit of a novice Pythoner and have recently been running a K-Means Clustering in Azure Machine Learning. That method doesn't suit the dataset I have due to noise and I'm thinking that DBSCAN may be the way to go. My question is the dataset is 940 columns by 104k rows so, as a bit of a newbie, I'm not sure of the best way to deal with a dataset this size. Any high level advice much appreciated
Thanks
Mads