Jaccard Similarity with Binary Data
The DBSCAN clustering algorithm has a built-in Jaccard distance metric.

from sklearn.cluster import DBSCAN
db = DBSCAN( metric='jaccard' ).fit(X)
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)


Where X is your dataset with the related columns you want to use.