Dec-19-2018, 12:37 AM
I would suggest you to apply some dimension reduction technique first. It might be useful to
explore the dataset using e.g. t-SNE, or even PCA. DBSCAN is a good choice, but you need to choose
appropriate metric to get it worked fine. You probably would need to scale the data before applying any clustering or dimension reduction technique. If you dataset is sparse, you could consider to apply NMDS-approach first. Everything depends on specificity of your dataset: what data types the columns have?! Are they all of numeric type or some columns have categorical data?!
explore the dataset using e.g. t-SNE, or even PCA. DBSCAN is a good choice, but you need to choose
appropriate metric to get it worked fine. You probably would need to scale the data before applying any clustering or dimension reduction technique. If you dataset is sparse, you could consider to apply NMDS-approach first. Everything depends on specificity of your dataset: what data types the columns have?! Are they all of numeric type or some columns have categorical data?!