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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.

Tâche

Swipe to start coding

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

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
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.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 4. Chapitre 2

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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.

Tâche

Swipe to start coding

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

Import SpectralClustering function from sklearn.cluster.
Create a SpectralClustering model with 4 clusters.
Fit the data and predict the labels. Save predicted labels within the 'prediction' column of data.
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.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 4. Chapitre 2

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous