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DBSCAN Advantages

DBSCAN is a fantastic machine learning algorithm that compliments other clustering models really well such as K-Means and Hierarchical (Agglomorative or Divisive) Clustering algorithms. When we look at DBSCAN’s working principals we see that it’s an algorithm that’s different from both K-Means and Hierarchical Clustering.

While K-Means is useful for creating spherical clusters from the entire dataset and Hierarchical Clustering is useful for obtaining different multiple levels of hierarchical clusters (similar to a tree structure where you can decide the tree depth), DBSCAN can create clusters around arbitrary shapes (like a polynomial, irregular snake like shape or circular peripheral) or overlapping clusters such as a region with a different density inside another shape.

1- Arbitrary Shapes

Probably the biggest selling point of DBSCAN algorithm is its ability to cluster arbitrary shapes and overlapping regions based on its density-based clustering technique.

This allows clustering in pretty much any shape whether it’s a triangle, ellipse, cross or just an arbitrary shape.


2- Optimization

DBSCAN algorithm has intuitive parameters which can be tuned to control the clustering  outcomes.

Epsilon can be used to define the distance to scan for members in a region with same density while min_samples is the parameter that defines the minimum amount of samples required for a member to be considered in the same density region.

3- Simple and Easy

Using as well as understanding DBSCAN is also easy.

Although the outcomes can be clustering of complex shaped regions. Pretty much all you need to do is to decide the epsilon (distance for scanning) and minimum sample amount of the cluster regions. If you don’t have a sophisticated guess this can also be an iterative process like most Machine Learning implementations and you can decide the optimum values as you get some initial output.

Outliers Detected

4- DBSCAN will leave out samples that don't fit the density rules

Unlike K-Means clustering algorithm, DBSCAN won’t include every single sample in the clusters formed in the end. If some of the samples don’t fit the distance and minimum sample rules these will be left out as a noise point. This working principal can be very useful in detecting outliers as well as leaving out unwanted noise in the data.


DBSCAN is a unique ML algorithm that has invaluable benefits in specific applications such as arbitrarily shaped clusters. In this article, we’ve explored its many pros and hopefully you can implement this unsupervised machine learning algorithm a little more confidently in different AI use-case scenarios. Also, we have other posts about DBSCAN and its inner workings.

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