Plots of data using DBSCAN algorithm not making sense I am using clustering for my data. Since the DBSCAN algorithm will also tell me an estimate of clusters that I can use, I have used DBSCAN. I have tried for the eps=[0.123,1,2] and min_smaples=[2,10,...60]. The print satatement in the below code prints 714 which is equal to the number of data-samples(rows). The code looks like:

dbscan = DBSCAN(eps=1, min_samples = 4)
clusters = dbscan.fit_predict(df)
print(len(clusters))
plt.scatter(df.iloc[:, 0], df.iloc[:, 1], c=clusters, cmap="plasma")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")


Sample plots for some of the different min_samples is shown below:






The parameters for the above plots are given below in the same pattern.


EPS=1, MIN_SAMPLES=2, EPS=1, MIN_SAMPLES=10
EPS=1, MIN_SAMPLES=20, EPS=1, MIN_SAMPLES=40


None of these plots are making sense to me from a clustering perspective. From this, I am forced to conclude that I cannot use clustering for the given data or I am doing it wrong. So, I need help with insights into the weird appearance of the above plots. 

Any help is appreciated
Dont try to visually confirm it.

You are plotting your clustering resutls in ONLY two dimensions and you expect that all of the information is in these two dimesnions. That is very unlikely. If you plot 3 dimensions you will see even  more seperability and it will make a bit more sense. In any case you need a metric for example Silhouette that tells you how well you clustered. Visualisation is just a sanity check if you know your features already.