Orange: Louvain Clustering - Revision history

Onnowpurbo: /* Contoh */

2020-04-14T03:21:59Z

Contoh

← Older revision		Revision as of 03:21, 14 April 2020
Line 24:		Line 24:
	==Contoh==		==Contoh==

	Widget Louvain Clustering mengkonversi dataset menjadi graph, dimana widget Louvain Clustering akan ~~menembukan~~ highly interconnected node. Kita bisa memvisualisasi graph yang dihasilkan menggunakan Widget Network Explorer dari Network addon.		Widget Louvain Clustering mengkonversi dataset menjadi graph, dimana widget Louvain Clustering akan menemukan highly interconnected node. Kita bisa memvisualisasi graph yang dihasilkan menggunakan Widget Network Explorer dari Network addon.

	[[File:Louvain-Example.png\|center\|600px\|thumb]]		[[File:Louvain-Example.png\|center\|600px\|thumb]]

Onnowpurbo at 06:21, 13 April 2020

2020-04-13T06:21:51Z

← Older revision		Revision as of 06:21, 13 April 2020
Line 1:		Line 1:
	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html

	~~Mengelompokan~~ item menggunakan algoritma Louvain clustering.		Widget Louvain Clustering mengelompokan item menggunakan algoritma Louvain clustering.

	==Input==		==Input==
Line 12:		Line 12:
	Graph (with the Network addon): the weighted k-nearest neighbor graph		Graph (with the Network addon): the weighted k-nearest neighbor graph

	Widget mengubah data input menjadi k-nearest neighbor graph. Untuk mempertahankan gagasan distance, Jaccard index untuk jumlah shared tetangga digunakan untuk menentukan batas (edge). Akhirnya, algoritma pendeteksian optimisasi komunitas modularitas diterapkan pada graph untuk memperoleh cluster dari node yang highly interconnected. Widget mengeluarkan set data baru di mana ~~icluster~~ index digunakan sebagai atribut meta.		Widget Louvain Clustering mengubah data input menjadi k-nearest neighbor graph. Untuk mempertahankan gagasan distance, Jaccard index untuk jumlah shared tetangga digunakan untuk menentukan batas (edge). Akhirnya, algoritma pendeteksian optimisasi komunitas modularitas diterapkan pada graph untuk memperoleh cluster dari node yang highly interconnected. Widget mengeluarkan set data baru di mana cluster index digunakan sebagai atribut meta.

	[[File:Louvain-stamped.png\|center\|~~200px~~\|thumb]]		[[File:Louvain-stamped.png\|center\|600px\|thumb]]

	* PCA processing is typically applied to the original data to remove noise.		* PCA processing is typically applied to the original data to remove noise.
Line 24:		Line 24:
	==Contoh==		==Contoh==

	Louvain Clustering mengkonversi dataset menjadi graph, dimana Louvain Clustering akan menembukan highly interconnected node. Kita bisa memvisualisasi graph yang dihasilkan menggunakan Widget Network Explorer dari Network addon.		Widget Louvain Clustering mengkonversi dataset menjadi graph, dimana widget Louvain Clustering akan menembukan highly interconnected node. Kita bisa memvisualisasi graph yang dihasilkan menggunakan Widget Network Explorer dari Network addon.

	[[File:Louvain-Example.png\|center\|~~200px~~\|thumb]]		[[File:Louvain-Example.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-03-05T05:01:57Z

Contoh

← Older revision		Revision as of 05:01, 5 March 2020
Line 24:		Line 24:
	==Contoh==		==Contoh==

	Louvain Clustering ~~converts the~~ dataset ~~into a~~ graph, ~~where it finds~~ highly interconnected ~~nodes~~. ~~We can visualize the~~ graph ~~itself using the~~ Network Explorer ~~from the~~ Network addon.		Louvain Clustering mengkonversi dataset menjadi graph, dimana Louvain Clustering akan menembukan highly interconnected node. Kita bisa memvisualisasi graph yang dihasilkan menggunakan Widget Network Explorer dari Network addon.

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

Onnowpurbo at 04:54, 5 March 2020

2020-03-05T04:54:59Z

← Older revision		Revision as of 04:54, 5 March 2020
Line 1:		Line 1:
	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html

	~~roups items using the~~ Louvain clustering ~~algorithm~~.		Mengelompokan item menggunakan algoritma Louvain clustering.

	==Input==		==Input==
Line 12:		Line 12:
	Graph (with the Network addon): the weighted k-nearest neighbor graph		Graph (with the Network addon): the weighted k-nearest neighbor graph

	~~The widget first converts the~~ input ~~data into a~~ k-nearest neighbor graph. ~~To preserve the notions of~~ distance, ~~the~~ Jaccard index ~~for the number of~~ shared ~~neighbors is used to weight the edges~~. ~~Finally~~, ~~a modularity optimization community detection algorithm is applied to the~~ graph ~~to retrieve clusters of~~ highly interconnected ~~nodes~~. ~~The widget outputs a new dataset in which the cluster~~ index ~~is used as a~~ meta ~~attribute~~.		Widget mengubah data input menjadi k-nearest neighbor graph. Untuk mempertahankan gagasan distance, Jaccard index untuk jumlah shared tetangga digunakan untuk menentukan batas (edge). Akhirnya, algoritma pendeteksian optimisasi komunitas modularitas diterapkan pada graph untuk memperoleh cluster dari node yang highly interconnected. Widget mengeluarkan set data baru di mana icluster index digunakan sebagai atribut meta.

	[[File:Louvain-stamped.png\|center\|200px\|thumb]]		[[File:Louvain-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 01:40, 29 January 2020

2020-01-29T01:40:22Z

← Older revision		Revision as of 01:40, 29 January 2020
Line 3:		Line 3:
	roups items using the Louvain clustering algorithm.		roups items using the Louvain clustering algorithm.

	~~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
	Graph (with the Network addon): the weighted k-nearest neighbor graph		Graph (with the Network addon): the weighted k-nearest neighbor graph

	The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.		The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.
Line 16:		Line 16:
	[[File:Louvain-stamped.png\|center\|200px\|thumb]]		[[File:Louvain-stamped.png\|center\|200px\|thumb]]

	PCA processing is typically applied to the original data to remove noise.		* PCA processing is typically applied to the original data to remove noise.
	The distance metric is used for finding specified number of nearest neighbors.		* The distance metric is used for finding specified number of nearest neighbors.
	The number of nearest neighbors to use to form the KNN graph.		* The number of nearest neighbors to use to form the KNN graph.
	Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.		* Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.
	When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.		* When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.

	==Contoh==		==Contoh==

Onnowpurbo at 02:00, 24 January 2020

2020-01-24T02:00:41Z

← Older revision		Revision as of 02:00, 24 January 2020
Line 10:		Line 10:

	Data: dataset with cluster index as a class attribute		Data: dataset with cluster index as a class attribute

	Graph (with the Network addon): the weighted k-nearest neighbor graph		Graph (with the Network addon): the weighted k-nearest neighbor graph

	The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.		The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.

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

	PCA processing is typically applied to the original data to remove noise.		PCA processing is typically applied to the original data to remove noise.

	The distance metric is used for finding specified number of nearest neighbors.		The distance metric is used for finding specified number of nearest neighbors.

	The number of nearest neighbors to use to form the KNN graph.		The number of nearest neighbors to use to form the KNN graph.

	Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.		Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.

	When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.		When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.

	~~Example~~		==Contoh==

	Louvain Clustering converts the dataset into a graph, where it finds highly interconnected nodes. We can visualize the graph itself using the Network Explorer from the Network addon.		Louvain Clustering converts the dataset into a graph, where it finds highly interconnected nodes. We can visualize the graph itself using the Network Explorer from the Network addon.

	~~../../_images/~~Louvain-Example.png		[[File:Louvain-Example.png\|center\|200px\|thumb]]
	~~References~~
			==Referensi==

	Blondel, Vincent D., et al. “Fast unfolding of communities in large networks.” Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.		Blondel, Vincent D., et al. “Fast unfolding of communities in large networks.” Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html roups items using the Louvain clustering algorithm. Inputs Data: input..."

2020-01-17T00:49:37Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html roups items using the Louvain clustering algorithm. Inputs Data: input..."

New page

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

roups items using the Louvain clustering algorithm.

Inputs

Data: input dataset

Outputs

Data: dataset with cluster index as a class attribute

Graph (with the Network addon): the weighted k-nearest neighbor graph

The widget first converts the input data into a k-nearest neighbor graph. To preserve the notions of distance, the Jaccard index for the number of shared neighbors is used to weight the edges. Finally, a modularity optimization community detection algorithm is applied to the graph to retrieve clusters of highly interconnected nodes. The widget outputs a new dataset in which the cluster index is used as a meta attribute.

../../_images/Louvain-stamped.png

PCA processing is typically applied to the original data to remove noise.

The distance metric is used for finding specified number of nearest neighbors.

The number of nearest neighbors to use to form the KNN graph.

Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points.

When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.

Example

Louvain Clustering converts the dataset into a graph, where it finds highly interconnected nodes. We can visualize the graph itself using the Network Explorer from the Network addon.

../../_images/Louvain-Example.png
References

Blondel, Vincent D., et al. “Fast unfolding of communities in large networks.” Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.

Lambiotte, Renaud, J-C. Delvenne, and Mauricio Barahona. “Laplacian dynamics and multiscale modular structure in networks.” arXiv preprint, arXiv:0812.1770 (2008).

==Referensi==

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

==Pranala Menarik==

* [[Orange]]