From 5dcaa8a5326a726a156f1784db0ccb22da6051e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Primo=C5=BE=20Godec?= Date: Fri, 12 Jul 2019 12:20:54 +0200 Subject: [PATCH] OWDBSCAN: Documentation --- doc/visual-programming/source/index.rst | 3 +- .../source/widgets/unsupervised/DBSCAN.md | 46 ++++++++++++++++++ .../unsupervised/images/dbscan-example.png | Bin 0 -> 76061 bytes .../unsupervised/images/dbscan-stamped.png | Bin 0 -> 34553 bytes 4 files changed, 48 insertions(+), 1 deletion(-) create mode 100644 doc/visual-programming/source/widgets/unsupervised/DBSCAN.md create mode 100644 doc/visual-programming/source/widgets/unsupervised/images/dbscan-example.png create mode 100644 doc/visual-programming/source/widgets/unsupervised/images/dbscan-stamped.png diff --git a/doc/visual-programming/source/index.rst b/doc/visual-programming/source/index.rst index ee0c3080e1b..b63dc8c609f 100644 --- a/doc/visual-programming/source/index.rst +++ b/doc/visual-programming/source/index.rst @@ -122,10 +122,11 @@ Unsupervised widgets/unsupervised/hierarchicalclustering widgets/unsupervised/kmeans widgets/unsupervised/louvainclustering + widgets/unsupervised/DBSCAN widgets/unsupervised/mds widgets/unsupervised/tsne widgets/unsupervised/manifoldlearning - + Evaluation ---------- diff --git a/doc/visual-programming/source/widgets/unsupervised/DBSCAN.md b/doc/visual-programming/source/widgets/unsupervised/DBSCAN.md new file mode 100644 index 00000000000..bb3ed06d287 --- /dev/null +++ b/doc/visual-programming/source/widgets/unsupervised/DBSCAN.md @@ -0,0 +1,46 @@ +DBSCAN +====== + +Groups items using the DBSCAN clustering algorithm. + +**Inputs** + +- Data: input dataset + +**Outputs** + +- Data: dataset with cluster index as a class attribute + +The widget applies the +[DBSCAN clustering](https://en.wikipedia.org/wiki/DBSCAN) algorithm to +the data and outputs a new dataset with cluster indices as a meta +attribute. The widget also shows the sorted graph with distances to +k-th nearest neighbors. With k values set to **Core point neighbors** +as suggested in the +[methods article](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf). +This gives the user the idea of an +ideal selection for **Neighborhood distance** setting. As suggested by +authors this parameter should be set to the first value in the first +"valley" in the graph. + +![](images/dbscan-stamped.png) + +1. Set *minimal number of core neighbors* for a cluster and *maximal +neighborhood distance. +2. Set the distance metric that is used in grouping the items. +3. If *Apply Automatically* is ticked, the widget will commit changes +automatically. Alternatively, click *Apply*. +4. The graph shows the distance to the k-th nearest neighbor. *k* is +set by the **Core point neighbor** option. With moving the black slider +left and right you can select the right **Neighbourhood distance**. + +Example +------- + +In the following example, we connected the File widget with selected +Iris dataset to the DBSCAN widget. In the DBSCAN widget, we set +**Core points neighbors** parameter to 5. And select the +**Neighbourhood distance** to the value in the first "valley" in the +graph. We show clusters in the Scatter Plot widget. + +![](images/dbscan-example.png) diff --git a/doc/visual-programming/source/widgets/unsupervised/images/dbscan-example.png b/doc/visual-programming/source/widgets/unsupervised/images/dbscan-example.png new file mode 100644 index 0000000000000000000000000000000000000000..90cfe393e5d8a468fbf1c15d3ebcf952afb32994 GIT binary patch literal 76061 zcmeFZbyQr-*66z$*FdnKfuO=Dx1{!yFcWVeqNYI4f8X&m4dw@W2Xo7oiOK^Ri z?7h#~hjUKG_ujp4-1|q?7<6~ltU0UdS2btVs_w-rWknfGG-5OW05BiRN~r<>qBZ~^ zl%l}Ha)ir3&tY$;?Rct}C`4-VtjE%bkd&^k)<(ZSxc;LGK&|b-A8Sf7mjw zo+*n#l%E|E>cJa+kp!?L4Hz7IW}?4)7w}MdN}rSL*i+FJUqKztiC91AAy#K(4WIfDz~!OlCqM38!2=L>=pr&uaBi^e7dwLeM| zgjBvxRo^y6A2|Q>x_QOYk&&+!KAmM>@@cfBi=rRAZD zakr0pd@YbSTg6FykoOLb?wL;}e92k#gKp|cRyXL=B+-X1*1ZO!hic$c>;^B~w!Knt z*R47}d?%syuv)l(S~(2>0(4etny#7(PXtUH>{yIU9gNLbJnbA|0|)>@qMnXMCN^fS z6vk#2R`$Y_dkrm=6jr9flp5R$YzmH&W|mg6-p*!Ey%p6=ylqVQO({i1(1bh%U`7Yr2niAmJ+74 zbaizUU}g32@L=)aWN~n|U}fj$=VxW(VCCRohLvD;@v?U{@?^Gmq58wfUv{L-Tuhv; 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