> For the complete documentation index, see [llms.txt](https://bata-mladen.gitbook.io/bata-mladen/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bata-mladen.gitbook.io/bata-mladen/notes/machine-learning/learning-algorythm-types/unsupervised-ml/dbscan.md).

# DBscan

Clusters are dense regions in the data space, separated by regions of the lower density of points. The **DBSCAN algorithm** is based on this intuitive notion of “clusters” and “noise”. The key idea is that for each point of a cluster, the neighborhood of a given radius has to contain at least a minimum number of points. 

## Parameters Required For DBSCAN Algorithm

1. **eps**: It defines the neighborhood around a data point.  One way to find the eps value is based on the ***k-distance graph***. 

   <figure><img src="/files/BfbSGDby6Ss4orWegkuA" alt=""><figcaption><p>eps area</p></figcaption></figure>
2. **MinPts**: Minimum number of neighbors (data points) within eps radius. As a general rule, the minimum MinPts can be derived from the number of dimensions D in the dataset as, MinPts >= D+1. The minimum value of MinPts must be chosen at least 3.

## Data points

In this algorithm, we have 3 types of data points.\
**Core Point**: A point is a core point if it has more than MinPts points within eps. \
**Border Point**: A point which has fewer than MinPts within eps but it is in the neighborhood of a core point. \
**Noise or outlier**: A point which is not a core point or border point.

![](https://media.geeksforgeeks.org/wp-content/uploads/20190418023034/781ff66c-b380-4a78-af25-80507ed6ff26.jpeg)

***

## Example:

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
from sklearn.cluster import DBSCAN

# Generate synthetic moon-shaped data
X, _ = make_moons(n_samples=1000, noise=0.1, random_state=42)

# Apply DBSCAN clustering
dbscan = DBSCAN(eps=0.2, min_samples=5)
y_pred = dbscan.fit_predict(X)

# Plot the clusters
plt.figure(figsize=(8, 6))
plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap='viridis', marker='o', edgecolors='k')
plt.title('DBSCAN Clustering')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()

```

***

## Visualization

```python
import matplotlib.pyplot as plt  #treba da se ispravi!!!

plt.scatter(X[:, 0], X[:, 1])
plt.show()
```