def cluster_mhlink(self, num_tuples=255, tuple_size=3, table_size=2**20, thres=0.7,
min_cluster_size=3):
"""
Clusters a database of mined lists using agglomerative clustering based on LSH
"""
models=la.mhlink_cluster(self.ldb, tuple_size, num_tuples, table_size,
la.list_overlap, thres, min_cluster_size)
la.listdb_apply_to_all(models, la.list_sort_by_frequency_back)
return L1LSH(ldb=models)
def cluster_sklearn(self, algorithm):
"""
Clusters a database of mined lists using the clustering methods available in scikit-learn
"""
csr = self.tocsr()
algorithm.fit(csr.toarray())
if hasattr(algorithm, 'cluster_centers_'):
ldb = ndarray_to_listdb(algorithm.cluster_centers_)
else:
ldb = centers_from_labels(csr, algorithm.labels_)
la.listdb_apply_to_all(ldb.ldb, la.list_sort_by_frequency_back)
return ldb
