DOC Clarify eps parameter importance in DBSCAN
@@ -35,8 +35,11 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
``metric='precomputed'``.
eps : float, optional
- The maximum distance between two samples for them to be considered
- as in the same neighborhood.
+ The maximum distance between two samples for one to be considered
+ as in the neighborhood of the other. This is not a maximum bound
+ on the distances of points within a cluster. This is the most
+ important DBSCAN parameter to choose appropriately for your data set
+ and distance function.
min_samples : int, optional
The number of samples (or total weight) in a neighborhood for a point
@@ -128,6 +131,10 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
In: Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
+
+ Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
+ DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
+ ACM Transactions on Database Systems (TODS), 42(3), 19.
"""
if not eps > 0.0:
raise ValueError("eps must be positive.")
@@ -195,8 +202,11 @@ class DBSCAN(BaseEstimator, ClusterMixin):
Parameters
----------
eps : float, optional
- The maximum distance between two samples for them to be considered
- as in the same neighborhood.
+ The maximum distance between two samples for one to be considered
+ as in the neighborhood of the other. This is not a maximum bound
+ on the distances of points within a cluster. This is the most
+ important DBSCAN parameter to choose appropriately for your data set
+ and distance function.
min_samples : int, optional
The number of samples (or total weight) in a neighborhood for a point
@@ -300,6 +310,10 @@ class DBSCAN(BaseEstimator, ClusterMixin):
Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
In: Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
+
+ Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
+ DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
+ ACM Transactions on Database Systems (TODS), 42(3), 19.
"""
def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
