Python: String clustering with scikit-learn's dbscan, using Levenshtein distance as metric:
From the scikit-learn faq you can do this by making a custom metric:
from leven import levenshtein import numpy as npfrom sklearn.cluster import dbscandata = ["ACCTCCTAGAAG", "ACCTACTAGAAGTT", "GAATATTAGGCCGA"]def lev_metric(x, y): i, j = int(x[0]), int(y[0]) # extract indices return levenshtein(data[i], data[j])X = np.arange(len(data)).reshape(-1, 1)dbscan(X, metric=lev_metric, eps=5, min_samples=2)
Try ELKI instead of sklearn.
It is the only tool I know that allows index accelerated DBSCAN with any metric.
It includes Levenshtein distance. You need to add an index to your database with -db.index. I always use the cover tree index (you need to choose the same distance for the index and for the algorithm, of course!)
You could use "pyfunc" distances and ball trees in sklearn, but performance was really bad because of the interpreter. Also, DBSCAN in sklearn is much more memory intensive.