Difference between revisions of "Orange: Louvain Clustering"

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html
 
Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/louvainclustering.html
  
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roups items using the Louvain clustering algorithm.
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Widget Louvain Clustering mengelompokan item menggunakan algoritma Louvain clustering.
  
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Inputs
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==Input==
  
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    Data: input dataset
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Data: input dataset
  
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Outputs
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==Output==
  
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    Data: dataset with cluster index as a class attribute
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Data: dataset with cluster index as a class attribute
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    Graph (with the Network addon): the weighted k-nearest neighbor graph
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Graph (with the Network addon): the weighted k-nearest neighbor graph
  
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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.
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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.
  
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[[File:Louvain-stamped.png|center|200px|thumb]]
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[[File:Louvain-stamped.png|center|600px|thumb]]
  
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    PCA processing is typically applied to the original data to remove noise.
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* PCA processing is typically applied to the original data to remove noise.
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    The distance metric is used for finding specified number of nearest neighbors.
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* The distance metric is used for finding specified number of nearest neighbors.
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    The number of nearest neighbors to use to form the KNN graph.
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* The number of nearest neighbors to use to form the KNN graph.
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    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.
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* 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.
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    When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.
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* When Apply Automatically is ticked, the widget will automatically communicate all changes. Alternatively, click Apply.
  
 
==Contoh==
 
==Contoh==
  
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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.
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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.
  
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[[File:Louvain-Example.png|center|200px|thumb]]
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[[File:Louvain-Example.png|center|600px|thumb]]
  
 
==Referensi==
 
==Referensi==

Latest revision as of 10:21, 14 April 2020

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

Widget Louvain Clustering mengelompokan item menggunakan algoritma Louvain clustering.

Input

Data: input dataset

Output

Data: dataset with cluster index as a class attribute
Graph (with the Network addon): the weighted k-nearest neighbor graph

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.

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.

Contoh

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.

Louvain-Example.png

Referensi

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

Pranala Menarik

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