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I am working on a project currently and I wish to cluster multi-dimensional data. I tried K-Means clustering and DBSCAN clustering, both being completely different algorithms.

The K-Means model returned a fairly good output, it returned 5 clusters but I have read that when the dimensionality is large, the Euclidean distance fails so I don't know if I can trust this model.

On trying the DBSCAN model, the model generated a lot of noise points and clustered a lot of points in one cluster. I tried the KNN dist plot method to find the optimal eps for the model but I can't seem to make the model work. This led to my conclusion that maybe the density of the points plotted is very high and maybe that is the reason I am getting a lot of points in one cluster.

For clustering, I am using 10 different columns of data. Should I change the algorithm I am using? What would be a better algorithm for multi-dimensional data and with less-varying density?

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  • $\begingroup$ It's not clear what you mean by low density difference. Please clarify, perhaps including a chart or scatterplot of the data to illustrate this explanation. $\endgroup$
    – user234562
    Commented Mar 31, 2020 at 12:04
  • $\begingroup$ What I mean by low density difference is that the density of points isn't varying much, they might be concentrated at some point. I'll try to add a scatter plot. $\endgroup$
    – Ashish Rao
    Commented Mar 31, 2020 at 13:05

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