Orange: Hierarchical Clustering - Revision history

Onnowpurbo: /* Contoh */

2020-04-14T01:53:09Z

Contoh

← Older revision		Revision as of 01:53, 14 April 2020
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	[[File:HierarchicalClustering-Example.png\|center\|600px\|thumb]]		[[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]]		[[File:HierarchicalClustering-Example2.png\|center\|600px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-14T01:48:46Z

Contoh

← Older revision		Revision as of 01:48, 14 April 2020
Line 40:		Line 40:
	[[File:HierarchicalClustering-Example.png\|center\|600px\|thumb]]		[[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]]		[[File:HierarchicalClustering-Example2.png\|center\|600px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-14T01:48:21Z

Contoh

← Older revision		Revision as of 01:48, 14 April 2020
Line 40:		Line 40:
	[[File:HierarchicalClustering-Example.png\|center\|600px\|thumb]]		[[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]]		[[File:HierarchicalClustering-Example2.png\|center\|600px\|thumb]]



	==Referensi==		==Referensi==

Onnowpurbo: /* Output */

2020-04-14T01:24:18Z

Output

← Older revision		Revision as of 01:24, 14 April 2020
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	Widget Hierarchical Clustering akan menghitung hierarchical clustering dari objek dengan tipe sembarang dari distance matrix dan menampilkan dendrogram-nya.		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:		* The widget supports four ways of measuring distances between clusters:

Onnowpurbo at 09:39, 7 April 2020

2020-04-07T09:39:53Z

← Older revision		Revision as of 09:39, 7 April 2020
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	~~Mengelompokan~~ item menggunakan algoritma hierarchical clustering.		Widget Hierarchical Clustering mengelompokan item menggunakan algoritma hierarchical clustering.

	==Input==		==Input==
Line 36:		Line 36:
	==Contoh==		==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]]

Onnowpurbo at 04:16, 5 March 2020

2020-03-05T04:16:55Z

← Older revision		Revision as of 04:16, 5 March 2020
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	~~Groups items using a~~ hierarchical clustering ~~algorithm~~.		Mengelompokan item menggunakan algoritma hierarchical clustering.

	==Input==		==Input==
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	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.		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\|200px\|thumb]]
Line 36:		Line 36:
	==Contoh==		==Contoh==

	~~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~~.		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.

	[[File:HierarchicalClustering-Example.png\|center\|200px\|thumb]]		[[File:HierarchicalClustering-Example.png\|center\|200px\|thumb]]

	~~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~~.		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.

	[[File:HierarchicalClustering-Example2.png\|center\|200px\|thumb]]		[[File:HierarchicalClustering-Example2.png\|center\|200px\|thumb]]

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

← Older revision		Revision as of 00:15, 17 January 2020
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	~~#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]]

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]]

← Older revision		Revision as of 01:37, 29 January 2020
Line 4:		Line 4:
	Groups items using a hierarchical clustering algorithm.		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.		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]]		[[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==		==Contoh==

← Older revision		Revision as of 01:29, 24 January 2020
Line 11:		Line 11:

	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.		The widget computes hierarchical clustering of arbitrary types of objects from a matrix of distances and shows a corresponding dendrogram.

	~~../../_images/~~HierarchicalClustering-stamped.png		[[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.

	~~Examples~~		==Contoh==

	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.		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		[[File:HierarchicalClustering-Example.png\|center\|200px\|thumb]]

	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.		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		[[File:HierarchicalClustering-Example2.png\|center\|200px\|thumb]]