FLASC

Flare-Sensitive Clustering (FLASC) adds an efficient post-processing step to the HDBSCAN* clustering algorithm to detect branching structures within clusters.

The flasc package is closely based on the hdbscan package and supports the same API, except sparse inputs, which are not supported yet.

from flasc import FLASC
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

data = np.load('./notebooks/data/flared_clusterable_data.npy')
clusterer = FLASC(min_cluster_size=15)
clusterer.fit(data)
colors = sns.color_palette('tab10', 23)
point_colors = [
   sns.desaturate(colors[l], p)
   for l, p in zip(clusterer.labels_, clusterer.probabilities_)
]
plt.scatter(data[:, 0], data[:, 1], 2, point_colors, alpha=0.5)
plt.axis('off')
plt.show()

• Examples
  • How FLASC works
  • Example: Detecting Diabetes Types
  • Example: Single Cell Gene Expression Trajectories
  • Example: Detecting Parkinson using Smart Phone Sensors
  • Example: Branches in 3D Horse
  • Comparing Branch Stability
  • Comparing Computational Performance
  • Multiprocessing Behaviour
• API Reference
  • FLASC
  • flasc()
  • Predictions