Orange: Hierarchical Clustering - Revision history

Onnowpurbo: /* Contoh */

2020-04-14T01:53:09Z

Contoh

Onnowpurbo: /* Contoh */

2020-04-14T01:48:46Z

Contoh

Onnowpurbo: /* Contoh */

2020-04-14T01:48:21Z

Contoh

Onnowpurbo: /* Output */

2020-04-14T01:24:18Z

Output

Onnowpurbo at 09:39, 7 April 2020

2020-04-07T09:39:53Z

Onnowpurbo at 04:16, 5 March 2020

2020-03-05T04:16:55Z

Onnowpurbo at 01:37, 29 January 2020

2020-01-29T01:37:06Z

Onnowpurbo at 01:29, 24 January 2020

2020-01-24T01:29:03Z

Onnowpurbo at 00:15, 17 January 2020

2020-01-17T00:15:45Z

Onnowpurbo: Onnowpurbo moved page Orange: Hierarchical Clustering to Orange: Hierarchical Clustering Example

2020-01-17T00:14:07Z

Onnowpurbo moved page Orange: Hierarchical Clustering to Orange: Hierarchical Clustering Example

New page

#REDIRECT [[Orange: Hierarchical Clustering Example]]

@@ Line 40: / Line 40: @@
 [[File:HierarchicalClustering-Example.png|center|600px|thumb]]
-Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File. Data instance di hitung jarak-nya menggunakan widget Distance yang kemudian dimasukan ke widget Hierarchical Clustering. Keluaran widget Hierarchical Clustering dimasukan ke widget Scatter Plot. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
+Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File. Data instance di hitung jarak-nya menggunakan widget Distances yang kemudian dimasukan ke widget Hierarchical Clustering. Keluaran widget Hierarchical Clustering dimasukan ke widget Scatter Plot. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
 [[File:HierarchicalClustering-Example2.png|center|600px|thumb]]

@@ Line 40: / Line 40: @@
 [[File:HierarchicalClustering-Example.png|center|600px|thumb]]
-Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File. Data instance di hitung jarak-nya menggunakan widget Distance yang kemudian dimasukan ke widget Hierarchical Clustering. Keluaran widget Hierarchical Clustering dimasukan ke widget Scatter Plot. sementara pada saat yang sama menerima sinyal instance yang dipilih dari widget Hierarchical Clustering. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
+Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File. Data instance di hitung jarak-nya menggunakan widget Distance yang kemudian dimasukan ke widget Hierarchical Clustering. Keluaran widget Hierarchical Clustering dimasukan ke widget Scatter Plot. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
 [[File:HierarchicalClustering-Example2.png|center|600px|thumb]]

@@ Line 40: / Line 40: @@
 [[File:HierarchicalClustering-Example.png|center|600px|thumb]]
-Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File, sementara pada saat yang sama menerima sinyal instance yang dipilih dari widget Hierarchical Clustering. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
+Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File. Data instance di hitung jarak-nya menggunakan widget Distance yang kemudian dimasukan ke widget Hierarchical Clustering. Keluaran widget Hierarchical Clustering dimasukan ke widget Scatter Plot. sementara pada saat yang sama menerima sinyal instance yang dipilih dari widget Hierarchical Clustering. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
 [[File:HierarchicalClustering-Example2.png|center|600px|thumb]]
 ==Referensi==

@@ Line 15: / Line 15: @@
 Widget Hierarchical Clustering akan menghitung hierarchical clustering dari objek dengan tipe sembarang dari distance matrix dan menampilkan dendrogram-nya.
-[[File:HierarchicalClustering-stamped.png|center|200px|thumb]]
+[[File:HierarchicalClustering-stamped.png|center|400px|thumb]]
 * The widget supports four ways of measuring distances between clusters:

@@ Line 2: / Line 2: @@
-Mengelompokan item menggunakan algoritma hierarchical clustering.
+Widget Hierarchical Clustering mengelompokan item menggunakan algoritma hierarchical clustering.
 ==Input==
@@ Line 36: / Line 36: @@
 ==Contoh==
-Workflow di bawah ini menunjukkan output Hierarchical Clustering untuk dataset Iris dalam widget Tabel Data. Kita melihat bahwa jika kita memilih Append cluster IDs dalam hierarchical clustering, kita bisa melihat kolom tambahan di Data Table bernama Cluster. Ini adalah cara untuk memeriksa bagaimana pengelompokan hierarkis mengelompokkan individual instance.
+Workflow di bawah ini menunjukkan output widget Hierarchical Clustering untuk dataset Iris dalam widget Data Table. Kita melihat bahwa jika kita memilih Append cluster IDs dalam widget Hierarchical Clustering, kita bisa melihat kolom tambahan di widget Data Table bernama Cluster. Ini adalah cara untuk memeriksa bagaimana widget Hierarchical Clustering mengelompokkan individual instance.
-[[File:HierarchicalClustering-Example.png|center|200px|thumb]]
+[[File:HierarchicalClustering-Example.png|center|600px|thumb]]
-Pada contoh kedua, kami me-load dataset Iris lagi, tetapi kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File, sementara pada saat yang sama menerima sinyal instance yang dipilih dari Hierarchical Clustering. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi.
+Dalam contoh di bawah ini, kita load dataset Iris, kali ini kita menambahkan Widget Scatter Plot, menampilkan semua instance dari widget File, sementara pada saat yang sama menerima sinyal instance yang dipilih dari widget Hierarchical Clustering. Dengan cara ini kita dapat mengamati posisi cluster yang dipilih dalam proyeksi widget Scatter Plot.
-[[File:HierarchicalClustering-Example2.png|center|200px|thumb]]
+[[File:HierarchicalClustering-Example2.png|center|600px|thumb]]

@@ Line 4: / Line 4: @@
 Groups items using a hierarchical clustering algorithm.
-Inputs
+==Input==
-    Distances: distance matrix
+ Distances: distance matrix
-Outputs
+==Output==
-    Selected Data: instances selected from the plot
+ Selected Data: instances selected from the plot
-    Data: data with an additional column showing whether an instance is selected
+ Data: data with an additional column showing whether an instance is selected
 The widget computes hierarchical clustering of arbitrary types of objects from a matrix of distances and shows a corresponding dendrogram.
@@ Line 17: / Line 17: @@
 [[File:HierarchicalClustering-stamped.png|center|200px|thumb]]
-    The widget supports four ways of measuring distances between clusters:
+* The widget supports four ways of measuring distances between clusters:
-        Single linkage computes the distance between the closest elements of the two clusters
+** Single linkage computes the distance between the closest elements of the two clusters
-        Average linkage computes the average distance between elements of the two clusters
+** Average linkage computes the average distance between elements of the two clusters
-        Weighted linkage uses the WPGMA method
+** Weighted linkage uses the WPGMA method
-        Complete linkage computes the distance between the clusters’ most distant elements
+** Complete linkage computes the distance between the clusters’ most distant elements
-    Labels of nodes in the dendrogram can be chosen in the Annotation box.
+* Labels of nodes in the dendrogram can be chosen in the Annotation box.
-    Huge dendrograms can be pruned in the Pruning box by selecting the maximum depth of the dendrogram. This only affects the display, not the actual clustering.
+* Huge dendrograms can be pruned in the Pruning box by selecting the maximum depth of the dendrogram. This only affects the display, not the actual clustering.
-    The widget offers three different selection methods:
+* The widget offers three different selection methods:
-        Manual (Clicking inside the dendrogram will select a cluster. Multiple clusters can be selected by holding Ctrl/Cmd. Each selected cluster is shown in a different color and is treated as a separate cluster in the output.)
+** Manual (Clicking inside the dendrogram will select a cluster. Multiple clusters can be selected by holding Ctrl/Cmd. Each selected cluster is shown in a different color and is treated as a separate cluster in the output.)
-        Height ratio (Clicking on the bottom or top ruler of the dendrogram places a cutoff line in the graph. Items to the right of the line are selected.)
+** Height ratio (Clicking on the bottom or top ruler of the dendrogram places a cutoff line in the graph. Items to the right of the line are selected.)
-        Top N (Selects the number of top nodes.)
+** Top N (Selects the number of top nodes.)
-    Use Zoom and scroll to zoom in or out.
+* Use Zoom and scroll to zoom in or out.
-    If the items being clustered are instances, they can be added a cluster index (Append cluster IDs). The ID can appear as an ordinary Attribute, Class attribute or a Meta attribute. In the second case, if the data already has a class attribute, the original class is placed among meta attributes.
+* If the items being clustered are instances, they can be added a cluster index (Append cluster IDs). The ID can appear as an ordinary Attribute, Class attribute or a Meta attribute. In the second case, if the data already has a class attribute, the original class is placed among meta attributes.
-    The data can be automatically output on any change (Auto send is on) or, if the box isn’t ticked, by pushing Send Data.
+* The data can be automatically output on any change (Auto send is on) or, if the box isn’t ticked, by pushing Send Data.
-    Clicking this button produces an image that can be saved.
+* Clicking this button produces an image that can be saved.
-    Produce a report.
+* Produce a report.
 ==Contoh==

← Older revision		Revision as of 00:15, 17 January 2020
Line 1:		Line 1:
−	~~#REDIRECT [[Orange~~: Hierarchical Clustering Example]]	+	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/hierarchicalclustering.html
		+
		+
		+	Groups items using a hierarchical clustering algorithm.
		+
		+	Inputs
		+
		+	Distances: distance matrix
		+
		+	Outputs
		+
		+	Selected Data: instances selected from the plot
		+
		+	Data: data with an additional column showing whether an instance is selected
		+
		+	The widget computes hierarchical clustering of arbitrary types of objects from a matrix of distances and shows a corresponding dendrogram.
		+
		+	../../_images/HierarchicalClustering-stamped.png
		+
		+	The widget supports four ways of measuring distances between clusters:
		+
		+	Single linkage computes the distance between the closest elements of the two clusters
		+
		+	Average linkage computes the average distance between elements of the two clusters
		+
		+	Weighted linkage uses the WPGMA method
		+
		+	Complete linkage computes the distance between the clusters’ most distant elements
		+
		+	Labels of nodes in the dendrogram can be chosen in the Annotation box.
		+
		+	Huge dendrograms can be pruned in the Pruning box by selecting the maximum depth of the dendrogram. This only affects the display, not the actual clustering.
		+
		+	The widget offers three different selection methods:
		+
		+	Manual (Clicking inside the dendrogram will select a cluster. Multiple clusters can be selected by holding Ctrl/Cmd. Each selected cluster is shown in a different color and is treated as a separate cluster in the output.)
		+
		+	Height ratio (Clicking on the bottom or top ruler of the dendrogram places a cutoff line in the graph. Items to the right of the line are selected.)
		+
		+	Top N (Selects the number of top nodes.)
		+
		+	Use Zoom and scroll to zoom in or out.
		+
		+	If the items being clustered are instances, they can be added a cluster index (Append cluster IDs). The ID can appear as an ordinary Attribute, Class attribute or a Meta attribute. In the second case, if the data already has a class attribute, the original class is placed among meta attributes.
		+
		+	The data can be automatically output on any change (Auto send is on) or, if the box isn’t ticked, by pushing Send Data.
		+
		+	Clicking this button produces an image that can be saved.
		+
		+	Produce a report.
		+
		+	Examples
		+
		+	The workflow below shows the output of Hierarchical Clustering for the Iris dataset in Data Table widget. We see that if we choose Append cluster IDs in hierarchical clustering, we can see an additional column in the Data Table named Cluster. This is a way to check how hierarchical clustering clustered individual instances.
		+
		+	../../_images/HierarchicalClustering-Example.png
		+
		+	In the second example, we loaded the Iris dataset again, but this time we added the Scatter Plot, showing all the instances from the File widget, while at the same time receiving the selected instances signal from Hierarchical Clustering. This way we can observe the position of the selected cluster(s) in the projection.
		+
		+	../../_images/HierarchicalClustering-Example2.png
		+
		+
		+
		+
		+	==Referensi==
		+
		+	* https://docs.biolab.si//3/visual-programming/widgets/unsupervised/hierarchicalclustering.html
		+
		+	==Pranala Menarik==
		+
		+	* [[Orange]]