Difference between revisions of "Orange: Silhouette Plot"

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A graphical representation of consistency within clusters of data.
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Widget Silhouette Plot menampilkan representasi grafis akan inkonsistensi dalam data cluster.
  
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Inputs
 
  
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    Data: input dataset
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==Input==
  
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Outputs
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Data: input dataset
  
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    Selected Data: instances selected from the plot
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==Output==
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    Data: data with an additional column showing whether a point is selected
 
  
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The Silhouette Plot widget offers a graphical representation of consistency within clusters of data and provides the user with the means to visually assess cluster quality. The silhouette score is a measure of how similar an object is to its own cluster in comparison to other clusters and is crucial in the creation of a silhouette plot. The silhouette score close to 1 indicates that the data instance is close to the center of the cluster and instances possessing the silhouette scores close to 0 are on the border between two clusters.
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Selected Data: instances selected from the plot
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Data: data with an additional column showing whether a point is selected
  
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[[File:SilhouettePlot-stamped.png|center|200px|thumb]]
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Widget Silhouette Plot menawarkan representasi grafis akan konsistensi dalam data cluster dan memberi pengguna sarana untuk menilai kualitas cluster secara visual. Silhouette score adalah ukuran seberapa mirip suatu objek dengan clusternya sendiri dibandingkan dengan cluster lain dan sangat penting dalam pembuatan Silhouette plot. Silhouette score mendekati 1 menunjukkan bahwa instance data dekat dengan pusat cluster dan instance yang memiliki Silhouette score mendekati 0 berada di perbatasan antara dua cluster.
  
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    Choose the distance metric. You can choose between:
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[[File:SilhouettePlot-stamped.png|center|600px|thumb]]
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        Euclidean (“straight line” distance between two points)
 
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        Manhattan (the sum of absolute differences for all attributes)
 
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        Cosine (1 - cosine of the angle between two vectors)
 
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    Select the cluster label. You can decide whether to group the instances by cluster or not.
 
  
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    Display options:
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* Choose the distance metric. You can choose between:
  
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        Choose bar width.
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** Euclidean (“straight line” distance between two points)
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        Annotations: annotate the silhouette plot.
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** Manhattan (the sum of absolute differences for all attributes)
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    Save Image saves the created silhouette plot to your computer in a .png or .svg format.
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** Cosine (1 - cosine of the angle between two vectors)
  
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    Produce a report.
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* Select the cluster label. You can decide whether to group the instances by cluster or not.
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* Display options:
  
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    Output:
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** Choose bar width.
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** Annotations: annotate the silhouette plot.
  
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        Add silhouette scores (good clusters have higher silhouette scores)
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* Save Image saves the created silhouette plot to your computer in a .png or .svg format.
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        By clicking Commit, changes are communicated to the output of the widget. Alternatively, tick the box on the left and changes will be communicated automatically.
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* Produce a report.
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    The created silhouette plot.
 
  
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==Contoh==
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* Output:
  
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In the snapshot below, we have decided to use the Silhouette Plot on the iris dataset. We selected data instances with low silhouette scores and passed them on as a subset to the Scatter Plot widget. This visualization only confirms the accuracy of the Silhouette Plot widget, as you can clearly see that the subset lies in the border between two clusters.
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** Add silhouette scores (good clusters have higher silhouette scores)
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** By clicking Commit, changes are communicated to the output of the widget. Alternatively, tick the box on the left and changes will be communicated automatically.
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[[File:SilhouettePlot-Example.png|center|200px|thumb]]
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** The created silhouette plot.
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If you are interested in other uses of the Silhouette Plot widget, feel free to explore our blog post.
 
  
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==Contoh==
  
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Dalam workflow di bawah ini, kita akan menggunakan widget Silhouette Plot pada dataset iris. Kita memilih instance data dengan silhouette score rendah dan meneruskannya subset dari widget Scatter Plot. Visualisasi ini hanya mengkonfirmasi keakuratan widget Silhouette Plot, karena anda dapat dengan jelas melihat bahwa subset terletak di perbatasan antara dua cluster.
  
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[[File:SilhouettePlot-Example.png|center|600px|thumb]]
  
 
==Referensi==
 
==Referensi==

Latest revision as of 11:13, 9 April 2020

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


Widget Silhouette Plot menampilkan representasi grafis akan inkonsistensi dalam data cluster.


Input

Data: input dataset

Output

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

Widget Silhouette Plot menawarkan representasi grafis akan konsistensi dalam data cluster dan memberi pengguna sarana untuk menilai kualitas cluster secara visual. Silhouette score adalah ukuran seberapa mirip suatu objek dengan clusternya sendiri dibandingkan dengan cluster lain dan sangat penting dalam pembuatan Silhouette plot. Silhouette score mendekati 1 menunjukkan bahwa instance data dekat dengan pusat cluster dan instance yang memiliki Silhouette score mendekati 0 berada di perbatasan antara dua cluster.

SilhouettePlot-stamped.png
  • Choose the distance metric. You can choose between:
    • Euclidean (“straight line” distance between two points)
    • Manhattan (the sum of absolute differences for all attributes)
    • Cosine (1 - cosine of the angle between two vectors)
  • Select the cluster label. You can decide whether to group the instances by cluster or not.
  • Display options:
    • Choose bar width.
    • Annotations: annotate the silhouette plot.
  • Save Image saves the created silhouette plot to your computer in a .png or .svg format.
  • Produce a report.
  • Output:
    • Add silhouette scores (good clusters have higher silhouette scores)
    • By clicking Commit, changes are communicated to the output of the widget. Alternatively, tick the box on the left and changes will be communicated automatically.
    • The created silhouette plot.

Contoh

Dalam workflow di bawah ini, kita akan menggunakan widget Silhouette Plot pada dataset iris. Kita memilih instance data dengan silhouette score rendah dan meneruskannya subset dari widget Scatter Plot. Visualisasi ini hanya mengkonfirmasi keakuratan widget Silhouette Plot, karena anda dapat dengan jelas melihat bahwa subset terletak di perbatasan antara dua cluster.

SilhouettePlot-Example.png

Referensi

Pranala Menarik

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