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Cluster Analysis in Python

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Peculiarity of Spectral Clustering

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Uppgift

Swipe to start coding

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

  1. Import SpectralClustering function from sklearn.cluster.
  2. Create a SpectralClustering model with 4 clusters.
  3. Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
  4. Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget to display the plot.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 2

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book
Peculiarity of Spectral Clustering

The result of the last chapter was great! Spectral clustering correctly figured out the structure of the clusters, unlike K-Means and K-Medoids algorithms.

Thus, spectral clustering is very useful in case of intersect/overlapping clusters or when you can not use mean points and the centers.

For example, let's explore such a case. Given the 2-D training set of points, the scatter plot for which is built below.

Seems like 4 circles, therefore 4 clusters, doesn't it? But that is what K-Means will show us.

Not what we expected to see. Let's see how will spectral clustering deal with this data.

Please note, that the spectral clustering algorithm may take a long time to perform since it is based on hard math.

Uppgift

Swipe to start coding

For the given set of 2-D points data perform a spectral clustering. Follow the next steps:

  1. Import SpectralClustering function from sklearn.cluster.
  2. Create a SpectralClustering model with 4 clusters.
  3. Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
  4. Build scatter plot with 'x' column on the x-axis 'y' column on the y-axis for each value of 'prediction' (separate color for each value). Do not forget to display the plot.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 2
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
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