Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Lære How Similar are the Results? | Hierarchical Clustering
Cluster Analysis in Python
course content

Kursusindhold

Cluster Analysis in Python

Cluster Analysis in Python

1. K-Means Algorithm
Exploratory Data AnalysisWhat is a K-Means Algorithm?Defining the Number of ClustersCase 1: Three Distinct ClustersCase 2: Four Distinct ClustersClustering Weather DataFebruary vs July Average TemperaturesVisualizing the Dynamics Across Clusters
2. K-Medoids Algorithm
What is a K-Medoids Algorithm?Cluster CentersSilhouette ScoresK-Medoids and the Weather DataMean Yearly Temperatures Across ClustersComparing the Dynamics
3. Hierarchical Clustering
What is a Hierarchical Clustering?DendrogramsSetting Parameters: LinkageRand IndexThe Weather Data and LinkagesDeciding the Number of ClustersWeather Data: Complete and Ward LinkagesHow Similar are the Results?
4. Spectral Clustering
What is a Spectral Clustering?Peculiarity of Spectral ClusteringSetting Parameters: AffinityDefining the Number of ClustersIs 4 the Optimal Number of Clusters?Comparing the Trends Across Clusters

book
How Similar are the Results?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Opgave

Swipe to start coding

Table

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

  1. Import functions needed:
  • rand_score from sklearn.metrics.
  • AgglomerativeClustering from sklearn.cluster.
  1. Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
  2. Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
  3. Compute the rand index using labels_complete and labels_ward.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 8
toggle bottom row

book
How Similar are the Results?

Well done! Let's look at the last line charts you built in the previous chapter.

As you can see, only the ward linkage could catch the 'downward up to July' trend. Both results are different. But let's find out how different they are using the rand index.

Opgave

Swipe to start coding

Table

Compute the rand index to compare the results of using complete and ward linkages. Follow the next steps:

  1. Import functions needed:
  • rand_score from sklearn.metrics.
  • AgglomerativeClustering from sklearn.cluster.
  1. Create two models model_complete and model_ward performing a hierarchical clustering with 4 clusters both and 'complete' and 'ward' linkages respectively.
  2. Fit the 3-14 columns of data to models and predict the labels. Save the labels for model_complete within labels_complete and for model_ward within labels_ward.
  3. Compute the rand index using labels_complete and labels_ward.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 8
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
some-alt