Permalink
Branch: master
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
105 lines (82 sloc) 4.97 KB

Frequently Asked Questions

Here we attempt to address some common questions, directing the user to some helpful answers.

Q: Most of data is classified as noise; why?

The amount of data classified as noise is controlled by the min_samples parameter. By default, if not otherwise set, this value is set to the same value as min_cluster_size. You can set it independently if you wish by specifying it separately. The lower the value, the less noise you'll get, but there are limits, and it is possible that you simply have noisy data. See :any:`_min_samples_label` for more details.

Q: I mostly just get one large cluster; I want smaller clusters.

If you are getting a single large cluster and a few small outlying clusters that means your data is essentially a large glob with some small outlying clusters -- there may be structure to the glob, but compared to how well separated those other small clusters are, it doesn't really show up. You may, however, want to get at that more fine grained structure. You can do that, and what you are looking for is leaf clustering :any:`_leaf_cluster_label` .

Q: HDBSCAN is failing to separate the clusters I think it should.

Density based clustering relies on having enough data to separate dense areas. In higher dimensional spaces this becomes more difficult, and hence requires more data. Quite possibly there is not enough data to make your clusters clearly separable. Consider the following plots:

images/generative_model_scatter.png

images/generative_model_kde.png

Four different generative models, when sampled, produce results that are hard to easily differentiate. The blue dataset is sampled from a mixture of three standard Gaussians centered at (-2, 0), (0,0) and (2,0); the green dataset is sampled from a mixture of two standard Gaussians centered at (-1,0) and (1,0); the red data is sampled from a multivariate Gaussian with covariance [2, 0; 0, 1]; the purple data is a single standard Gaussian with uniform background noise.

Despite the generate model having clearly different "clusters", without more data we simply cannot differentiate between these models, and hence no density based clustering will manage cluster these according to the model.

Q: I am not getting the claimed performance. Why not?

The most likely explanation is to do with the dimensionality of your input data. While HDBSCAN can perform well on low to medium dimensional data the performance tends to decrease significantly as dimension increases. In general HDBSCAN can do well on up to around 50 or 100 dimensional data, but performance can see significant decreases beyond that. Of course a lot is also dataset dependent, so you can still get good performance even on high dimensional data, but it is no longer guaranteed.

Q: I want to predict the cluster of a new unseen point. How do I do this?

This is possible via the function :func:`~hdbscan.prediction.approximate_predict`. Note that you either need to set prediction_data=True on initialization of your clusterer object, or run the generate_prediction_data method after fitting. With that done you can run :func:`~hdbscan.prediction.approximate_predict` with the model and any new data points you wish to predict. Note that this differs from re-running HDBSCAN with the new points added since no new clusters will be considered -- instead the new points will be labelled according to the clusters already labelled by the model.

Q: Haversine metric is not clustering my Lat-Lon data correctly.

The Haversine metric as implemented supports coordinates in radians. That means you'll need to convert your latitude and longitude data into radians before passing it in to HDBSCAN.

Q: I want to cite this software in my journal publication. How do I do that?

If you have used this codebase in a scientific publication and wish to cite it, please use the Journal of Open Source Software article.

L. McInnes, J. Healy, S. Astels, hdbscan: Hierarchical density based clustering In: Journal of Open Source Software, The Open Journal, volume 2, number 11. 2017

BibTeX:

@article{McInnes2017,
  doi = {10.21105/joss.00205},
  url = {https://doi.org/10.21105%2Fjoss.00205},
  year  = {2017},
  month = {mar},
  publisher = {The Open Journal},
  volume = {2},
  number = {11},
  author = {Leland McInnes and John Healy and Steve Astels},
  title = {hdbscan: Hierarchical density based clustering},
  journal = {The Journal of Open Source Software}
}