Difference between revisions of "Orange: DBSCAN"

Latest revision as of 10:41, 14 April 2020

Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/DBSCAN.html

Widget DBSCAN mengelompokan item menggunakan algoritma DBSCAN clustering.

Input

Data: input dataset

Output

Data: dataset with cluster index as a class attribute

Widget DBSCAN menerapkan algoritma DBSCAN clustering untuk data dan mengeluarkan dataset baru dengan indeks cluster sebagai atribut meta. Widget DBSCAN juga menunjukkan grafik yang diurutkan dengan jarak ke k-th tetangga terdekat. Dengan nilai-nilai k diatur ke Core point neighbors seperti yang disarankan dalam artikel metode ini. Ini memberi pengguna bayangan akan pilihan ideal untuk Neighborhood distance setting. Seperti yang disarankan oleh penemu algoritma ini, parameter ini harus ditetapkan ke nilai pertama di "valley" pertama dalam grafik.

Set minimal number of core neighbors for a cluster and *maximal neighborhood distance.
Set the distance metric that is used in grouping the items.
If Apply Automatically is ticked, the widget will commit changes automatically. Alternatively, click Apply.
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.

Contoh

Dalam contoh berikut, kita menghubungkan widget File dengan dataset Iris yang dipilih ke widget DBSCAN. Di widget DBSCAN, kita menetapkan Core points neighbors parameter ke 5. Dan pilih Neighbourhood distance ke nilai di "valley" pertama dalam graph. Kita menampilkan cluster dalam widget Scatter Plot.

Referensi

https://docs.biolab.si//3/visual-programming/widgets/unsupervised/DBSCAN.html

Pranala Menarik

Orange

@@ Line 2: / Line 2: @@
-Groups items using the DBSCAN clustering algorithm.
+Widget DBSCAN mengelompokan item menggunakan algoritma DBSCAN clustering.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-Outputs
+==Output==
-    Data: dataset with cluster index as a class attribute
+ Data: dataset with cluster index as a class attribute
-The widget applies the DBSCAN clustering 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. 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.
+Widget DBSCAN menerapkan algoritma DBSCAN clustering untuk data dan mengeluarkan dataset baru dengan indeks cluster sebagai atribut meta. Widget DBSCAN juga menunjukkan grafik yang diurutkan dengan jarak ke k-th tetangga terdekat. Dengan nilai-nilai k diatur ke Core point neighbors seperti yang disarankan dalam artikel metode ini. Ini memberi pengguna bayangan akan pilihan ideal untuk  Neighborhood distance setting. Seperti yang disarankan oleh penemu algoritma ini, parameter ini harus ditetapkan ke nilai pertama di "valley" pertama dalam grafik.
-../../_images/dbscan-stamped.png
+[[File:Dbscan-stamped.png|center|600px|thumb]]
-    Set minimal number of core neighbors for a cluster and *maximal neighborhood distance.
+* Set minimal number of core neighbors for a cluster and *maximal neighborhood distance.
+* Set the distance metric that is used in grouping the items.
-    Set the distance metric that is used in grouping the items.
+* If Apply Automatically is ticked, the widget will commit changes automatically. Alternatively, click Apply.
+* 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.
-    If Apply Automatically is ticked, the widget will commit changes automatically. Alternatively, click Apply.
-    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
+==Contoh==
+Dalam contoh berikut, kita menghubungkan widget File dengan dataset Iris yang dipilih ke widget DBSCAN. Di widget DBSCAN, kita menetapkan Core points neighbors parameter ke 5. Dan pilih Neighbourhood distance ke nilai di "valley" pertama dalam graph. Kita menampilkan cluster dalam widget Scatter Plot.
+[[File:Dbscan-example.png|center|600px|thumb]]
 ==Referensi==