Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Note: This implementation used from Iris dataset

Requirements

1-Python3.6+

2-Numpy

3-Sklearn

4-Math

Run

$ python3 main.py

Result:

In Terminal:

Epsilon: 0.39

MinPoints: 4

Rate: 80.0% (Approximate)

Cluster count: 3

Note: Number of all created clusters.

Cluster 1th: 10

Note: Number of first cluster points.

Cluster 2th: 25

Note: Number of second cluster points.

Cluster 3th: 15

Note: Number of third cluster points.

Noise count: 5

Note: Number of noise.

Meysam Alipuor

meysam.alipuor@gmail.com

https://github.com/ameysam/DBSCAN