> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# DBSCAN

<Tip>
  The following content is a read-only preview of an executable Jupyter notebook.

  To run this notebook interactively:

  1. Go to [**Wherobots Cloud**](https://cloud.wherobots.com).
  2. Start a runtime.
  3. Open the notebook.
  4. In the Jupyter Launcher:
     1. Click **File > Open Path**.
     2. Paste the following path to access this notebook: `examples/Analyzing_Data/Clustering_DBSCAN.ipynb`
     3. Click **Enter**.
</Tip>

DBSCAN is a popular algorithm for finding clusters of spatial data. It identifies core points that have enough (defined by the user) neighbors within some distance (also user defined). Points that are not core points but are within the distance of a core point are considered border points of the cluster. Points that are not core points and are not within the distance of a core point are considered outliers and not part of any cluster.

The algorithm requires two parameters:

* `epsilon` - The farthest apart two points can be while still being considered connected or related. `epsilon` must be a positive double float.
* `minPoints` - The minimum number of neighbor points (as determined by epsilon). A point needs `minPoints` neighbors to be considered a core point. `minPoints` must be a positive integer.

## Example overview

In this example, we will generate some random data and use DBSCAN to cluster that data. Then, we'll visualize the clusters using a scatter plot.

This demo is derived from the [scikit-learn DBSCAN demo](https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html).

```python theme={"system"}
%pip install scikit-learn
```

# Define Sedona Context

```python theme={"system"}
from sedona.spark import *

config = SedonaContext.builder().getOrCreate()
sedona = SedonaContext.create(config)
```

## Data Generation

In the following code section, we'll generate some data using sklearn's `make_blobs` function. We've set the data to consist of 750 points with 3 clusters. After clustering the data, we'll  visualize it in `pyplot`.

```python theme={"system"}
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

center_clusters = [[1, 1], [-1, -1], [1, -1]]
feature_matrix, labels_true = make_blobs(
   n_samples=750, centers=center_clusters, cluster_std=0.4, random_state=0
)

feature_matrix = StandardScaler().fit_transform(feature_matrix)

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

## Clustering

In the following section, we'll use the DBSCAN implementation in Wherobots to cluster the data in a dataframe, setting `epsilon` to `0.3` and `minPoints` to `10`.

Wherobots' DBSCAN returns outliers by default.

```python theme={"system"}
import pyspark.sql.functions as f
from sedona.spark import *

df = sedona.createDataFrame(feature_matrix).select(ST_MakePoint("_1", "_2").alias("geometry"))
clusters_df = dbscan(df, 0.3, 10, include_outliers=True)

```

```python theme={"system"}
clusters_df.show()
```

## Visualization

Finally, we'll visualize the clusters using geopandas. Some manipulations are made to the data to improve the clarity of the visualization.

```python theme={"system"}
import geopandas as gpd
import pyspark.sql.types as t

pdf = (clusters_df
       .withColumn("isCore", (f.col("isCore").cast(t.IntegerType()) + 1) * 40)
       .withColumn("cluster", f.hash("cluster").cast(t.StringType()))
       .toPandas()
      )
gdf = gpd.GeoDataFrame(pdf, geometry="geometry")

gdf.plot(
    figsize=(10, 8),
    column="cluster",
    markersize=gdf['isCore'],
    edgecolor='lightgray',
)
```