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Cluster Analysis in Python

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Deciding the Number of Clusters

Well done! Let's look one more time at all the dendrograms for the weather data.

As you can see, the single linkage method's dendrogram is unreadable. The average linkage method most likely led us to three clusters (if you draw a horizontal line between 75 and 100 you will intersect one blue line, and two green. The complete and ward linkages methods lead us to 4 clusters. For complete linkage, you can draw the horizontal line between 120 and 150 (it will intersect two orange and two green lines), and between 400 and 600 for ward linkage. Let's see what will be the results of using three clusters with average linkage.

Note, that in the previous sections we considered the cases of 5 or 4 clusters. Let's see how it will work now.

Aufgabe

Swipe to start coding

Table
  1. Import the AgglomerativeClustering function from sklearn.cluster.
  2. Create AgglomerativeClustering model object named model with 3 clusters and using 'average' linkage.
  3. Fit the numerical data (columns 3 - 14) to model and predict the labels. Save predicted labels as the 'prediction' column of data.
  4. For modified DataFrame monthly_data group the observations of columns from col by 'prediction' column, and calculate the mean within each group.
  5. Build line plot 'Month' vs 'Temp' for each value of 'Group' using monthly_data DataFrame.

Lösung

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Abschnitt 3. Kapitel 6
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book
Deciding the Number of Clusters

Well done! Let's look one more time at all the dendrograms for the weather data.

As you can see, the single linkage method's dendrogram is unreadable. The average linkage method most likely led us to three clusters (if you draw a horizontal line between 75 and 100 you will intersect one blue line, and two green. The complete and ward linkages methods lead us to 4 clusters. For complete linkage, you can draw the horizontal line between 120 and 150 (it will intersect two orange and two green lines), and between 400 and 600 for ward linkage. Let's see what will be the results of using three clusters with average linkage.

Note, that in the previous sections we considered the cases of 5 or 4 clusters. Let's see how it will work now.

Aufgabe

Swipe to start coding

Table
  1. Import the AgglomerativeClustering function from sklearn.cluster.
  2. Create AgglomerativeClustering model object named model with 3 clusters and using 'average' linkage.
  3. Fit the numerical data (columns 3 - 14) to model and predict the labels. Save predicted labels as the 'prediction' column of data.
  4. For modified DataFrame monthly_data group the observations of columns from col by 'prediction' column, and calculate the mean within each group.
  5. Build line plot 'Month' vs 'Temp' for each value of 'Group' using monthly_data DataFrame.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 6
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
Wir sind enttäuscht, dass etwas schief gelaufen ist. Was ist passiert?
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