https://github.com/christopherjenness/DBCV
Python implementation of Density-Based Clustering Validation
https://github.com/christopherjenness/DBCV
clustering clustering-validation dbcv density-based-clustering machine-learning
Last synced: 5 months ago
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Python implementation of Density-Based Clustering Validation
- Host: GitHub
- URL: https://github.com/christopherjenness/DBCV
- Owner: christopherjenness
- License: mit
- Created: 2017-03-19T19:53:22.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-12-19T04:45:21.000Z (almost 2 years ago)
- Last Synced: 2024-08-04T04:01:23.015Z (about 1 year ago)
- Topics: clustering, clustering-validation, dbcv, density-based-clustering, machine-learning
- Language: Python
- Size: 125 KB
- Stars: 144
- Watchers: 11
- Forks: 40
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DBCV
Python implementation of Density-Based Clustering Validation
## Source
Moulavi, Davoud, et al. "Density-based clustering validation." Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014.
[PDF](http://epubs.siam.org/doi/pdf/10.1137/1.9781611973440.96)
## What is DBCV
How do you validate clustering assignmnets from unsupervised learning algorithms? A common method is the [Silhoette Method](https://en.wikipedia.org/wiki/Silhouette_(clustering)), which provides an objective score between -1 and 1 on the quality of clustering. The silhouette value measures how well an object is classified in its own cluster instead of neighboring clusters. The silhouette (and most other popular methods) work very well on globular clusters, but can fail on non-glubular clusters such as:
Here, we implement DBCV which can validate clustering assignments on non-globular, arbitrarily shaped clusters (such as the example above). In essence, DBCV computes two values:
* The density **within** a cluster
* The density **between** clusters
High density within a cluster, and low density between clusters indicates good clustering assignments.
## Example
Here, I deliberately picked an example of clusters that density based clustering works well on.
```python
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
n_samples=150
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
X = noisy_moons[0]
plt.scatter(X[:,0], X[:,1])
plt.show()
```
What happens when we try K-means clustering on these non-globular clusters?
```python
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=2)
kmeans_labels = kmeans.fit_predict(X)
plt.scatter(X[:,0], X[:,1], c=kmeans_labels)
plt.show()
```
...Not so great. What about [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan), a density based clustering method?
```python
import hdbscan
hdbscanner = hdbscan.HDBSCAN()
hdbscan_labels = hdbscanner.fit_predict(X)
plt.scatter(X[:,0], X[:,1], c=hdbscan_labels)
```
That's pretty good. To assess the quality of clustering, using Density-Based Clustering Validation, we call `DBCV`
```python
from scipy.spatial.distance import euclidean
kmeans_score = DBCV(X, kmeans_labels, dist_function=euclidean)
hdbscan_score = DBCV(X, hdbscan_labels, dist_function=euclidean)
print(kmeans_score, hdbscan_score)
```
K means returns a DBCV score of **-0.71**, and HDBSCAN returns a score of **0.60**.