# DBSCAN considers all data points noise for reduced time series data [closed]

I had a data matrix 609 rows × 264 columns, time-series data. Data was reduced using t-SNE algorithm to 3 dimensions. When being clustered I get zero clusters, where all data points are considered noise. I tried increasing eps until I reached 1.0, and I get same results for min_samples=2: zero clusters, all noise data.

As a side note, I ran DBSCAN on t-SNE-reduced 2D data and I got (not so good but) more decent results.

My question is how can the eps be 1.0, and I get no clusters for min_samples=2?

## closed as off-topic by Nick Cox, jbowman, Michael Chernick, Peter Flom♦Jan 25 '18 at 14:47

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• DBSCAN, although conceptually great, is very sensitive to parameters. I recommend to define a param grid such as eps = [0.01, 0.1, 1, 10] and min_samples = [1,2,4,8,16,32,64] and plot the results for all possible combinations. – Nikolas Rieble Jan 23 '18 at 12:32
• Also i recommend to transform your time-series into a feature-space first (mean, max, min, var, kurt, etc. ) and then cluster in feature-space – Nikolas Rieble Jan 23 '18 at 12:34
• The eps parameter in DBSCAN can go above 1. – Stephan Kolassa Jan 24 '18 at 7:51