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.
Swipe to start coding
For the given set of 2-D points data perform a spectral clustering. Follow the next steps:
- Import
SpectralClusteringfunction fromsklearn.cluster. - Create a
SpectralClusteringmodel with 4 clusters. - Fit the
dataand predict the labels. Save predicted labels within the'prediction'column ofdata. - 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.
Løsning
Takk for tilbakemeldingene dine!
single
Spør AI
Spør AI
Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår
Awesome!
Completion rate improved to 3.57Peculiarity of Spectral Clustering
Sveip for å vise menyen
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.
Swipe to start coding
For the given set of 2-D points data perform a spectral clustering. Follow the next steps:
- Import
SpectralClusteringfunction fromsklearn.cluster. - Create a
SpectralClusteringmodel with 4 clusters. - Fit the
dataand predict the labels. Save predicted labels within the'prediction'column ofdata. - 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.
Løsning
Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenforTakk for tilbakemeldingene dine!
Awesome!
Completion rate improved to 3.57single
