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# 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***.
eps area
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.
***
## 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()
```
---
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