Orange: Radviz - Revision history

Onnowpurbo: /* Output */

2020-04-11T03:06:38Z

Output

← Older revision		Revision as of 03:06, 11 April 2020
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	Components: Radviz vectors		Components: Radviz vectors

	Radviz (Hoffman et al. 1997) adalah teknik visualisasi multi-dimensi non-linear yang dapat menampilkan data yang didefinisikan oleh tiga variabel atau lebih dalam proyeksi 2 dimensi. Variabel yang divisualisasikan disajikan sebagai titik anchor (jangkar) dengan jarak yang sama di sekitar keliling unit lingkaran. ~~Contoh~~ data ditampilkan sebagai titik di dalam lingkaran, dengan posisi mereka ditentukan oleh metafora dari fisika: setiap titik dipegang di tempat dengan pegas yang terpasang di ujung lain ke variabel anchor (jangkar). Kekakuan masing-masing pegas sebanding dengan nilai variabel yang sesuai dan titik berakhir pada posisi di mana gaya pegas berada dalam kesetimbangan. Sebelum visualisasi, nilai-nilai variabel diskalakan untuk berada di antara 0 dan 1. Data ~~instance~~ yang dekat dengan serangkaian variabel anchor (jangkar) memiliki nilai lebih tinggi untuk variabel-variabel ini daripada yang lain.		Radviz (Hoffman et al. 1997) adalah teknik visualisasi multi-dimensi non-linear yang dapat menampilkan data yang didefinisikan oleh tiga variabel atau lebih dalam proyeksi 2 dimensi. Variabel yang divisualisasikan disajikan sebagai titik anchor (jangkar) dengan jarak yang sama di sekitar keliling unit lingkaran. Instance data ditampilkan sebagai titik di dalam lingkaran, dengan posisi mereka ditentukan oleh metafora dari fisika: setiap titik dipegang di tempat dengan pegas yang terpasang di ujung lain ke variabel anchor (jangkar). Kekakuan masing-masing pegas sebanding dengan nilai variabel yang sesuai dan titik berakhir pada posisi di mana gaya pegas berada dalam kesetimbangan. Sebelum visualisasi, nilai-nilai variabel diskalakan untuk berada di antara 0 dan 1. Instance Data yang dekat dengan serangkaian variabel anchor (jangkar) memiliki nilai lebih tinggi untuk variabel-variabel ini daripada yang lain.

	Cuplikan yang ditunjukkan di bawah ini menunjukkan widget Radviz dengan visualisasi dataset dari functional genomics (Brown et al. 2000). Dalam visualisasi khusus ini instance data diwarnai sesuai dengan class yang sesuai, dan ruang visualisasi diwarnai sesuai dengan probabilitas class yang dihitung. Perhatikan bahwa visualisasi khusus dengan sangat baik memisahkan data instance dari class yang berbeda, membuat visualisasi menarik dan sangat informatif.		Cuplikan yang ditunjukkan di bawah ini menunjukkan widget Radviz dengan visualisasi dataset dari functional genomics (Brown et al. 2000). Dalam visualisasi khusus ini instance data diwarnai sesuai dengan class yang sesuai, dan ruang visualisasi diwarnai sesuai dengan probabilitas class yang dihitung. Perhatikan bahwa visualisasi khusus dengan sangat baik memisahkan data instance dari class yang berbeda, membuat visualisasi menarik dan sangat informatif.

Onnowpurbo: /* Output */

2020-04-11T00:08:11Z

Output

← Older revision		Revision as of 00:08, 11 April 2020
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	[[File:Radviz-Brown.png\|center\|600px\|thumb]]		[[File:Radviz-Brown.png\|center\|600px\|thumb]]

	~~Just like all point-based visualizations~~, ~~this~~ widget ~~includes tools for~~ intelligent data visualization (VizRank, ~~see~~ Leban et al. 2006) ~~and an~~ interface ~~for~~ explorative data analysis - ~~selection of~~ data ~~points in visualization~~. ~~Just like the~~ Scatter Plot widget~~, it can be used to find a~~ set ~~of variables that would result in an interesting visualization~~. ~~The~~ Radviz ~~graph above is according to this definition an example of a very good visualization~~, ~~while the one below~~ - ~~where we show an VizRank’s~~ interface (Suggest ~~features button~~) ~~with a list of~~ 3-~~attribute visualizations and their scores~~ - ~~is not~~.		Sama seperti semua visualisasi berbasis poin, widget RadViz mencakup tool untuk intelligent data visualization (VizRank, lihat Leban et al. 2006) dan interface untuk explorative data analysis - pemilihan titik data dalam visualisasi. Sama seperti widget Scatter Plot, widget RadViz dapat digunakan untuk menemukan satu set variabel yang akan menghasilkan visualisasi yang menarik. Grafik Radviz di atas sesuai dengan definisi ini contoh dari visualisasi yang sangat baik, sedangkan yang di bawah ini - di mana kami menunjukkan interface VizRank (tombol Suggest feature) dengan daftar visualisasi 3-atribut dan nilainya - tidak semenarik gambar di atas.

	[[File:Radviz-Brown-2.png\|center\|600px\|thumb]]		[[File:Radviz-Brown-2.png\|center\|600px\|thumb]]

Onnowpurbo: /* Output */

2020-04-11T00:04:36Z

Output

← Older revision		Revision as of 00:04, 11 April 2020
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	Radviz (Hoffman et al. 1997) adalah teknik visualisasi multi-dimensi non-linear yang dapat menampilkan data yang didefinisikan oleh tiga variabel atau lebih dalam proyeksi 2 dimensi. Variabel yang divisualisasikan disajikan sebagai titik anchor (jangkar) dengan jarak yang sama di sekitar keliling unit lingkaran. Contoh data ditampilkan sebagai titik di dalam lingkaran, dengan posisi mereka ditentukan oleh metafora dari fisika: setiap titik dipegang di tempat dengan pegas yang terpasang di ujung lain ke variabel anchor (jangkar). Kekakuan masing-masing pegas sebanding dengan nilai variabel yang sesuai dan titik berakhir pada posisi di mana gaya pegas berada dalam kesetimbangan. Sebelum visualisasi, nilai-nilai variabel diskalakan untuk berada di antara 0 dan 1. Data instance yang dekat dengan serangkaian variabel anchor (jangkar) memiliki nilai lebih tinggi untuk variabel-variabel ini daripada yang lain.		Radviz (Hoffman et al. 1997) adalah teknik visualisasi multi-dimensi non-linear yang dapat menampilkan data yang didefinisikan oleh tiga variabel atau lebih dalam proyeksi 2 dimensi. Variabel yang divisualisasikan disajikan sebagai titik anchor (jangkar) dengan jarak yang sama di sekitar keliling unit lingkaran. Contoh data ditampilkan sebagai titik di dalam lingkaran, dengan posisi mereka ditentukan oleh metafora dari fisika: setiap titik dipegang di tempat dengan pegas yang terpasang di ujung lain ke variabel anchor (jangkar). Kekakuan masing-masing pegas sebanding dengan nilai variabel yang sesuai dan titik berakhir pada posisi di mana gaya pegas berada dalam kesetimbangan. Sebelum visualisasi, nilai-nilai variabel diskalakan untuk berada di antara 0 dan 1. Data instance yang dekat dengan serangkaian variabel anchor (jangkar) memiliki nilai lebih tinggi untuk variabel-variabel ini daripada yang lain.

	~~The snapshot shown below shows a~~ Radviz ~~widget with a visualization of the~~ dataset ~~from~~ functional genomics (Brown et al. 2000). ~~In this particular visualization the~~ data ~~instances are colored according to the corresponding~~ class, ~~and the visualization space is colored according to the computed~~ class ~~probability~~. ~~Notice that the particular visualization very nicely separates~~ data ~~instances of different~~ class, ~~making the visualization interesting and potentially informative~~.		Cuplikan yang ditunjukkan di bawah ini menunjukkan widget Radviz dengan visualisasi dataset dari functional genomics (Brown et al. 2000). Dalam visualisasi khusus ini instance data diwarnai sesuai dengan class yang sesuai, dan ruang visualisasi diwarnai sesuai dengan probabilitas class yang dihitung. Perhatikan bahwa visualisasi khusus dengan sangat baik memisahkan data instance dari class yang berbeda, membuat visualisasi menarik dan sangat informatif.

	[[File:Radviz-Brown.png\|center\|600px\|thumb]]		[[File:Radviz-Brown.png\|center\|600px\|thumb]]

Onnowpurbo: /* Output */

2020-04-10T23:50:30Z

Output

← Older revision		Revision as of 23:50, 10 April 2020
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	Components: Radviz vectors		Components: Radviz vectors

	Radviz (Hoffman et al. 1997) ~~is a~~ non-linear ~~multi-dimensional visualization technique that can display~~ data ~~defined by three or more variables in a~~ 2~~-dimensional projection~~. ~~The visualized variables are presented as~~ anchor ~~points equally spaced around the perimeter of a~~ unit ~~circle~~. ~~Data instances are shown as points inside the circle~~, ~~with their positions determined by a metaphor from physics~~: ~~each point is held in place with springs that are attached at the other end to the variable anchors~~. ~~The stiffness of each spring is proportional to the value of the corresponding variable and the point ends up at the position where the spring forces are in equilibrium~~. ~~Prior to visualization~~, ~~variable values are scaled to lie between~~ 0 ~~and~~ 1. Data ~~instances that are close to a set of variable anchors have higher values for these variables than for the others~~.		Radviz (Hoffman et al. 1997) adalah teknik visualisasi multi-dimensi non-linear yang dapat menampilkan data yang didefinisikan oleh tiga variabel atau lebih dalam proyeksi 2 dimensi. Variabel yang divisualisasikan disajikan sebagai titik anchor (jangkar) dengan jarak yang sama di sekitar keliling unit lingkaran. Contoh data ditampilkan sebagai titik di dalam lingkaran, dengan posisi mereka ditentukan oleh metafora dari fisika: setiap titik dipegang di tempat dengan pegas yang terpasang di ujung lain ke variabel anchor (jangkar). Kekakuan masing-masing pegas sebanding dengan nilai variabel yang sesuai dan titik berakhir pada posisi di mana gaya pegas berada dalam kesetimbangan. Sebelum visualisasi, nilai-nilai variabel diskalakan untuk berada di antara 0 dan 1. Data instance yang dekat dengan serangkaian variabel anchor (jangkar) memiliki nilai lebih tinggi untuk variabel-variabel ini daripada yang lain.

	The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.		The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.
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	[[File:Radviz-Brown-2.png\|center\|600px\|thumb]]		[[File:Radviz-Brown-2.png\|center\|600px\|thumb]]


	==Referensi==		==Referensi==

Onnowpurbo at 23:47, 10 April 2020

2020-04-10T23:47:37Z

← Older revision		Revision as of 23:47, 10 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/radviz.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/radviz.html

	Radviz ~~vizualization with~~ explorative data analysis ~~and~~ intelligent data visualization ~~enhancements~~.		Widget Radviz mem-visualisasi dengan explorative data analysis dan intelligent data visualization enhancement.

	==Input==		==Input==

Onnowpurbo at 23:45, 10 April 2020

2020-04-10T23:45:50Z

← Older revision		Revision as of 23:45, 10 April 2020
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	The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.		The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.

	[[File:Radviz-Brown.png\|center\|~~200px~~\|thumb]]		[[File:Radviz-Brown.png\|center\|600px\|thumb]]

	Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank, see Leban et al. 2006) and an interface for explorative data analysis - selection of data points in visualization. Just like the Scatter Plot widget, it can be used to find a set of variables that would result in an interesting visualization. The Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank’s interface (Suggest features button) with a list of 3-attribute visualizations and their scores - is not.		Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank, see Leban et al. 2006) and an interface for explorative data analysis - selection of data points in visualization. Just like the Scatter Plot widget, it can be used to find a set of variables that would result in an interesting visualization. The Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank’s interface (Suggest features button) with a list of 3-attribute visualizations and their scores - is not.

	[[File:Radviz-Brown-2.png\|center\|~~200px~~\|thumb]]		[[File:Radviz-Brown-2.png\|center\|600px\|thumb]]

Onnowpurbo at 02:34, 28 January 2020

2020-01-28T02:34:16Z

← Older revision		Revision as of 02:34, 28 January 2020
Line 3:		Line 3:
	Radviz vizualization with explorative data analysis and intelligent data visualization enhancements.		Radviz vizualization with explorative data analysis and intelligent data visualization enhancements.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
	Data Subset: subset of instances		Data Subset: subset of instances

	~~Outputs~~		==Output==

	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot
	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected
	Components: Radviz vectors		Components: Radviz vectors

	Radviz (Hoffman et al. 1997) is a non-linear multi-dimensional visualization technique that can display data defined by three or more variables in a 2-dimensional projection. The visualized variables are presented as anchor points equally spaced around the perimeter of a unit circle. Data instances are shown as points inside the circle, with their positions determined by a metaphor from physics: each point is held in place with springs that are attached at the other end to the variable anchors. The stiffness of each spring is proportional to the value of the corresponding variable and the point ends up at the position where the spring forces are in equilibrium. Prior to visualization, variable values are scaled to lie between 0 and 1. Data instances that are close to a set of variable anchors have higher values for these variables than for the others.		Radviz (Hoffman et al. 1997) is a non-linear multi-dimensional visualization technique that can display data defined by three or more variables in a 2-dimensional projection. The visualized variables are presented as anchor points equally spaced around the perimeter of a unit circle. Data instances are shown as points inside the circle, with their positions determined by a metaphor from physics: each point is held in place with springs that are attached at the other end to the variable anchors. The stiffness of each spring is proportional to the value of the corresponding variable and the point ends up at the position where the spring forces are in equilibrium. Prior to visualization, variable values are scaled to lie between 0 and 1. Data instances that are close to a set of variable anchors have higher values for these variables than for the others.

Onnowpurbo at 01:31, 23 January 2020

2020-01-23T01:31:35Z

← Older revision		Revision as of 01:31, 23 January 2020
Line 6:		Line 6:

	Data: input dataset		Data: input dataset

	Data Subset: subset of instances		Data Subset: subset of instances

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	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	Components: Radviz vectors		Components: Radviz vectors

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	The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.		The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.

	~~../../_images/~~Radviz-Brown.png		[[File:Radviz-Brown.png\|center\|200px\|thumb]]

	Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank, see Leban et al. 2006) and an interface for explorative data analysis - selection of data points in visualization. Just like the Scatter Plot widget, it can be used to find a set of variables that would result in an interesting visualization. The Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank’s interface (Suggest features button) with a list of 3-attribute visualizations and their scores - is not.		Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank, see Leban et al. 2006) and an interface for explorative data analysis - selection of data points in visualization. Just like the Scatter Plot widget, it can be used to find a set of variables that would result in an interesting visualization. The Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank’s interface (Suggest features button) with a list of 3-attribute visualizations and their scores - is not.

	~~../../_images/~~Radviz-Brown-2.png		[[File:Radviz-Brown-2.png\|center\|200px\|thumb]]
	~~References~~

	~~Hoffman, P. E. et al. (1997) DNA visual and analytic data mining. In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441.~~

	~~Brown, M. P., W. N. Grundy et al. (2000). “Knowledge-based analysis of microarray gene expression data by using support vector machines.” Proc Natl Acad Sci U S A 97(1): 262-7.~~

	~~Leban, G., B. Zupan et al. (2006). “VizRank: Data Visualization Guided by Machine Learning.” Data Mining and Knowledge Discovery 13(2): 119-136.~~

	~~Mramor, M., G. Leban, J. Demsar, and B. Zupan. Visualization-based cancer microarray data classification analysis. Bioinformatics 23(16): 2147-2154, 2007.~~



	==Referensi==		==Referensi==

	* https://docs.~~biolab~~.~~si//3/~~visual-~~programming/widgets/visualize/radviz~~.~~html~~		Hoffman, P. E. et al. (1997) DNA visual and analytic data mining. In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441.

	~~==Pranala Menarik==~~		Brown, M. P., W. N. Grundy et al. (2000). “Knowledge-based analysis of microarray gene expression data by using support vector machines.” Proc Natl Acad Sci U S A 97(1): 262-7.

	* [[Orange]]		Leban, G., B. Zupan et al. (2006)

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2020-01-12T13:21:37Z

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/radviz.html

Radviz vizualization with explorative data analysis and intelligent data visualization enhancements.

Inputs

Data: input dataset

Data Subset: subset of instances

Outputs

Selected Data: instances selected from the plot

Data: data with an additional column showing whether a point is selected

Components: Radviz vectors

Radviz (Hoffman et al. 1997) is a non-linear multi-dimensional visualization technique that can display data defined by three or more variables in a 2-dimensional projection. The visualized variables are presented as anchor points equally spaced around the perimeter of a unit circle. Data instances are shown as points inside the circle, with their positions determined by a metaphor from physics: each point is held in place with springs that are attached at the other end to the variable anchors. The stiffness of each spring is proportional to the value of the corresponding variable and the point ends up at the position where the spring forces are in equilibrium. Prior to visualization, variable values are scaled to lie between 0 and 1. Data instances that are close to a set of variable anchors have higher values for these variables than for the others.

The snapshot shown below shows a Radviz widget with a visualization of the dataset from functional genomics (Brown et al. 2000). In this particular visualization the data instances are colored according to the corresponding class, and the visualization space is colored according to the computed class probability. Notice that the particular visualization very nicely separates data instances of different class, making the visualization interesting and potentially informative.

../../_images/Radviz-Brown.png

Just like all point-based visualizations, this widget includes tools for intelligent data visualization (VizRank, see Leban et al. 2006) and an interface for explorative data analysis - selection of data points in visualization. Just like the Scatter Plot widget, it can be used to find a set of variables that would result in an interesting visualization. The Radviz graph above is according to this definition an example of a very good visualization, while the one below - where we show an VizRank’s interface (Suggest features button) with a list of 3-attribute visualizations and their scores - is not.

../../_images/Radviz-Brown-2.png
References

Hoffman, P. E. et al. (1997) DNA visual and analytic data mining. In the Proceedings of the IEEE Visualization. Phoenix, AZ, pp. 437-441.

Brown, M. P., W. N. Grundy et al. (2000). “Knowledge-based analysis of microarray gene expression data by using support vector machines.” Proc Natl Acad Sci U S A 97(1): 262-7.

Leban, G., B. Zupan et al. (2006). “VizRank: Data Visualization Guided by Machine Learning.” Data Mining and Knowledge Discovery 13(2): 119-136.

Mramor, M., G. Leban, J. Demsar, and B. Zupan. Visualization-based cancer microarray data classification analysis. Bioinformatics 23(16): 2147-2154, 2007.

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/visualize/radviz.html

==Pranala Menarik==

* [[Orange]]