DBSCAN clustering

DBSCAN stands for density-based spatial clustering of applications with noise. It identifies clusters of data points based on their density in the feature space. Unlike k-means, DBSCAN does not require specifying the number of clusters beforehand and can handle clusters of arbitrary shapes.

Core concepts

To comprehend DBSCAN, we need to be familiar with its core concepts:

• Epsilon ($\boldsymbol{\varepsilon}$): The radius that defines the neighborhood around a data point.

• MinPts: The minimum number of data points required within the $\varepsilon$-neighborhood to form a core point.

• Core point: A data point with at least MinPts points within its $\varepsilon$-neighborhood, including itself.

• Border point: A data point that lies within the ε-neighborhood of a core point but has fewer than MinPts points in its $\varepsilon$-neighborhood.

• Noise point: A data point that is neither a core point nor a border point.

Working of DBSCAN clustering

DBSCAN works in the following way:

• It starts by identifying core samples or points in the dataset. A core sample or point has at least min_samples or MinPts points around it within a distance of eps $\boldsymbol{\varepsilon}$.

• Once we identify a core sample, we examine its neighbors and add them to the cluster if they meet the core sample criteria.

• Then, the cluster is expanded so that we can add non-core samples to it. These samples can be reached directly from the core samples within a distance of eps $\boldsymbol{\varepsilon}$. However, they are not core samples themselves. These points are also called border points in some literature.

• Once we have identified all the clusters, along with their core and non-core samples, the remaining samples are considered noise or outliers.

Advantages

DBSCAN offers several advantages over other clustering algorithms:

• It doesn't require a predefined number of clusters, making it suitable for varied datasets.

• It handles clusters of different shapes and sizes.

• It is robust to outliers and noise due to the noise point classification.

• It has efficient runtime complexity, making it suitable for large datasets.

Disadvantages

• It requires to carefully select eps $\boldsymbol{\varepsilon}$and MinPts, depending on the domain, for good results. If $\boldsymbol{\varepsilon}$ is set too small, some clusters may be overlooked, and if it is too large, multiple clusters may be merged into one. Similarly, the MinPts parameter can affect the ability to identify meaningful clusters.

• If the density of clusters varies significantly, it can be challenging for DBSCAN to identify clusters accurately. Setting a single $\boldsymbol{\varepsilon}$ value may not be appropriate for all clusters.

Conclusion

In conclusion, DBSCAN is a versatile clustering algorithm with several advantages for uncovering hidden patterns within datasets. Through its density-based approach, DBSCAN can identify clusters of arbitrary shapes and handle noisy data, making it well-suited for various data analysis tasks.