Spectral clustering is one of the most popular modern clustering algorithms. Typically spectral clustering requires number of clusters manually.
auto_spectral_clustering predicts number of clusters based on the eigengap(often referred to as spectral gap).
A Tutorial on Spectral Clustering, 2007, Luxburg, Ulrike[1] http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5b0%5d.pdf
New BSD License.
The eigengap heuristic usually works well if the data contains very well pronounced clusters, but in ambiguous cases it also returns ambiguous results.[1]
auto_spectral_clustering is tested under Python 3.3.5.
It requires NumPy, SciPy, scikit-learn. If you test it, it also requires matplotlib.
from autosp import predict_k
from sklearn.cluster import SpectralClustering
k = predict_k(affinity_matrix)
sc = SpectralClustering(n_clusters=k,
affinity="precomputed",
assign_labels="kmeans").fit(affinity_matrix)
labels_pred = sc.labels_
You can change number_of_blobs(artificial datasets) and test it!!
test.py
if __name__ == "__main__":
# Generate artificial datasets.
number_of_blobs = 5 # You can change this!!
data, labels_true = make_blobs(n_samples=number_of_blobs * 10,
centers=number_of_blobs)
