{"action":"opened","issue":{"url":"https://api.github.com/repos/lmcinnes/hdbscan/issues/44","repository_url":"https://api.github.com/repos/lmcinnes/hdbscan","labels_url":"https://api.github.com/repos/lmcinnes/hdbscan/issues/44/labels{/name}","comments_url":"https://api.github.com/repos/lmcinnes/hdbscan/issues/44/comments","events_url":"https://api.github.com/repos/lmcinnes/hdbscan/issues/44/events","html_url":"https://github.com/lmcinnes/hdbscan/issues/44","id":164531501,"number":44,"title":"The case of unclustered data for HDBSCAN","user":{"login":"Pantamis","id":13965245,"avatar_url":"https://avatars.githubusercontent.com/u/13965245?v=3","gravatar_id":"","url":"https://api.github.com/users/Pantamis","html_url":"https://github.com/Pantamis","followers_url":"https://api.github.com/users/Pantamis/followers","following_url":"https://api.github.com/users/Pantamis/following{/other_user}","gists_url":"https://api.github.com/users/Pantamis/gists{/gist_id}","starred_url":"https://api.github.com/users/Pantamis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Pantamis/subscriptions","organizations_url":"https://api.github.com/users/Pantamis/orgs","repos_url":"https://api.github.com/users/Pantamis/repos","events_url":"https://api.github.com/users/Pantamis/events{/privacy}","received_events_url":"https://api.github.com/users/Pantamis/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-07-08T13:31:18Z","updated_at":"2016-07-08T13:31:18Z","closed_at":null,"body":"Hi,\r\n\r\nFirst, thank you for this awesome implementation of HDBSCAN. It is certainly one of the best and most generic clustering algorithm and having this high performance implementation is cool with SciKit. \r\n\r\nMy datasets are groups of large time series with the same length (between 2000 and 15000 time points and 20-200 series in a dataset). Some series are really similar and i want to regroup them. In such high dimensions and so few series, cluster are not spherical and i don''t know how many clusters there are. At first, i tried DBSCAN but i need to take count of variable density of my clusters that i saw with a dendrogram of hierarchical clustering. That's why i tried HDBSCAN now and it seems to be a good one for my problem.\r\n\r\nHowever, i have datasets with only one cluster of series (at sight...) and HDBSCAN return more clusters than one. So i took a look at the implementation and i saw that you exclude the root of the `condensed_tree` so that the algorithm will never return only one cluster in your implementation.\r\n\r\nI tried to edit it myself in the file` _hdbscan_tree.pyx` (i added the root, remplaced by zeros the NaN in births list and deleted the last child for which stability is not well-defined). I succeed to \"unlock\" the possibility but i experiment issues with some other datasets with several clusters for which  the results of your version HDBSCAN are correct, the results are different with mine without reason (the root cluster is not stable enough but in my version many little clusters are created).\r\n\r\nSo here is my request : is it possible for you to add a parameter to choose when to exclude the root of the `condensed_tree` or not in the evaluation of the number of clusters ? I saw that you provided classes to make some choice but it's a bit difficult to deal with stability computations in the framework. I think my request is not a big deal to code.\r\n\r\nI will continue to work on it to make a custom version where this case is allowed because i really need it but it should be great for other to have the choice for this particular case. In my opinion, stability of the root cluster makes sense and it must be considered as the others, but i understand why enforce the clustering.\r\n\r\nThank you again for this so cool implementation, i can provide you what i change to add this possibility if you wish,\r\n\r\nBest,"}}
{"actor":{"display_login":"Pantamis"}}