DBSCAN: Finding Cluster of any shape

Exploring how DBSCAN uses density, not distance, to find clusters of any shape from theory and code to real-world applications like anomaly detection and geospatial mapping. Before diving into the code or real-world applications, it’s essential to understand what DBSCAN actually is and why it stands apart from traditional clustering techniques. What is DBSCAN At its core, DBSCAN short for Density-Based Spatial Clustering of Applications with Noise is a powerful algorithm that clusters data not based on shape or center points, but on the density of data points in a region. Let’s break down the key concepts that make DBSCAN unique. Density-Based Clustering : Instead of grouping points based of distance from a center(like K-MEANS). This algorithm groups points based on how crowded a region is No need for predefined number of clusters : Unlike k-means which needs to figure out the number of clusters. DBSCAN is able to get the different clusters based on density of points in a region It is like traversing through all the points noting the number of times you encountered regions of high density Identifies Noise and Outliers : DBSCAN is able to naturally identifies points that do not belong to any cluster like isolated data or anomalies . these are labelled as noise and hence a good tool for anomaly detection It can find clusters of any shape : It is not limited to circular or spherical clusters. It is able to detect clusters of any shape like spirals and other complex irregular shapes Core, Border, and Noise Points : DBSCAN classifies points into 3 types Types of Points in DBSCAN Core points : Have enough neighbors nearby (defined by ε and MinPts). ε is a defined distance used to determine the neighbors of a given point and MinPts is the minimum number of neighbor a core point must have to form a dense region Border points : Close to a core point but not dense enough on their own. Noise points : Points that are not close to any dense region. Code implementation 1.using numpy ALGORITHM According to this algorithm a cluster is a continious region of high density(contains certain number of points whose distant from core point is less that a certain number ε -For each instance(point) counts how many instances are located within a small distance ε (epsilon) from it. This region is called the instance’s ε- neighborhood. -If an instance has at least MinPts instances in its ε-neighborhood (including itself), then it is considered a core instance. In other words, core instances are those that are located in dense regions. -All instances in the same neighborhood will be assigned to the same cluster. this neighborhood may contain points that are core points to other neighborhoods -Any instance that is not a core instance and does not have one in its neighborhood is considered an anomaly.

May 8, 2025 - 18:22

Exploring how DBSCAN uses density, not distance, to find clusters of any shape from theory and code to real-world applications like anomaly detection and geospatial mapping.

Before diving into the code or real-world applications, it’s essential to understand what DBSCAN actually is and why it stands apart from traditional clustering techniques.

What is DBSCAN

At its core, DBSCAN short for Density-Based Spatial Clustering of Applications with Noise is a powerful algorithm that clusters data not based on shape or center points, but on the density of data points in a region. Let’s break down the key concepts that make DBSCAN unique.

Density-Based Clustering : Instead of grouping points based of distance from a center(like K-MEANS). This algorithm groups points based on how crowded a region is
No need for predefined number of clusters : Unlike k-means which needs to figure out the number of clusters. DBSCAN is able to get the different clusters based on density of points in a region

It is like traversing through all the points noting the number of times you encountered regions of high density

Identifies Noise and Outliers : DBSCAN is able to naturally identifies points that do not belong to any cluster like isolated data or anomalies . these are labelled as noise and hence a good tool for anomaly detection
It can find clusters of any shape : It is not limited to circular or spherical clusters. It is able to detect clusters of any shape like spirals and other complex irregular shapes
Core, Border, and Noise Points : DBSCAN classifies points into 3 types

Types of Points in DBSCAN

Core points : Have enough neighbors nearby (defined by ε and MinPts). ε is a defined distance used to determine the neighbors of a given point and MinPts is the minimum number of neighbor a core point must have to form a dense region
Border points : Close to a core point but not dense enough on their own.
Noise points : Points that are not close to any dense region.

Code implementation

1.using numpy

ALGORITHM According to this algorithm a cluster is a continious region of high density(contains certain number of points whose distant from core point is less that a certain number ε

-For each instance(point) counts how many instances are located within a small distance ε (epsilon) from it. This region is called the instance’s ε- neighborhood.
-If an instance has at least MinPts instances in its ε-neighborhood (including itself), then it is considered a core instance. In other words, core instances are those that are located in dense regions.
-All instances in the same neighborhood will be assigned to the same cluster. this neighborhood may contain points that are core points to other neighborhoods
-Any instance that is not a core instance and does not have one in its neighborhood is considered an anomaly.