What is DBSCAN and how does it detect outliers?

What is DBSCAN and how does it detect outliers?

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It identifies clusters based on the density of data points and can effectively detect outliers as points that do not belong to any cluster.

How does DBSCAN differ from other clustering methods?

Unlike methods like K-means, which require specifying the number of clusters in advance, DBSCAN identifies clusters based on the density of data points, making it more effective for irregularly shaped clusters and outlier detection.

What parameters do I need to set for DBSCAN?

The two main parameters are 'eps', which defines the maximum distance between two samples for them to be considered as in the same neighborhood, and 'min_samples', which is the minimum number of samples in a neighborhood for a point to be considered a core point.

Can you give an example of how to implement DBSCAN in Python?

Sure! You can use the DBSCAN class from the sklearn.cluster module. Here's a simple example:

from sklearn.cluster import DBSCAN
import numpy as np

# Sample data
X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])

# DBSCAN model
model = DBSCAN(eps=3, min_samples=2)
model.fit(X)

# Labels
print(model.labels_)